{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "c979f719-a7bd-4392-86f9-f699220b2c93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATEFP20</th>\n",
       "      <th>COUNTYFP20</th>\n",
       "      <th>TRACTCE20</th>\n",
       "      <th>GEOID20</th>\n",
       "      <th>NAME20</th>\n",
       "      <th>NAMELSAD20</th>\n",
       "      <th>MTFCC20</th>\n",
       "      <th>FUNCSTAT20</th>\n",
       "      <th>ALAND20</th>\n",
       "      <th>AWATER20</th>\n",
       "      <th>...</th>\n",
       "      <th>P0050002</th>\n",
       "      <th>P0050003</th>\n",
       "      <th>P0050004</th>\n",
       "      <th>P0050005</th>\n",
       "      <th>P0050006</th>\n",
       "      <th>P0050007</th>\n",
       "      <th>P0050008</th>\n",
       "      <th>P0050009</th>\n",
       "      <th>P0050010</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>42</td>\n",
       "      <td>095</td>\n",
       "      <td>017703</td>\n",
       "      <td>42095017703</td>\n",
       "      <td>177.03</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>3708021</td>\n",
       "      <td>9639</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>POLYGON ((-75.41090 40.65013, -75.40742 40.651...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>42</td>\n",
       "      <td>101</td>\n",
       "      <td>012204</td>\n",
       "      <td>42101012204</td>\n",
       "      <td>122.04</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>879459</td>\n",
       "      <td>56473</td>\n",
       "      <td>...</td>\n",
       "      <td>313</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>313</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>POLYGON ((-75.22066 40.00394, -75.21923 40.004...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>42</td>\n",
       "      <td>129</td>\n",
       "      <td>804300</td>\n",
       "      <td>42129804300</td>\n",
       "      <td>8043</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>1014739</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-79.55534 40.29320, -79.55529 40.293...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>42</td>\n",
       "      <td>129</td>\n",
       "      <td>804801</td>\n",
       "      <td>42129804801</td>\n",
       "      <td>8048.01</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>6852606</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-79.62842 40.30682, -79.62838 40.307...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>42</td>\n",
       "      <td>129</td>\n",
       "      <td>805200</td>\n",
       "      <td>42129805200</td>\n",
       "      <td>8052</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>1873022</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>POLYGON ((-79.88358 40.15396, -79.88353 40.154...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 345 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  STATEFP20 COUNTYFP20 TRACTCE20      GEOID20   NAME20    NAMELSAD20 MTFCC20  \\\n",
       "0        42        095    017703  42095017703   177.03  Census Tract   G5020   \n",
       "1        42        101    012204  42101012204   122.04  Census Tract   G5020   \n",
       "2        42        129    804300  42129804300     8043  Census Tract   G5020   \n",
       "3        42        129    804801  42129804801  8048.01  Census Tract   G5020   \n",
       "4        42        129    805200  42129805200     8052  Census Tract   G5020   \n",
       "\n",
       "  FUNCSTAT20  ALAND20  AWATER20  ... P0050002 P0050003 P0050004 P0050005  \\\n",
       "0          S  3708021      9639  ...        0        0        0        0   \n",
       "1          S   879459     56473  ...      313        0        0      313   \n",
       "2          S  1014739         0  ...        0        0        0        0   \n",
       "3          S  6852606         0  ...        0        0        0        0   \n",
       "4          S  1873022         0  ...        0        0        0        0   \n",
       "\n",
       "  P0050006 P0050007 P0050008 P0050009 P0050010  \\\n",
       "0        0        3        0        0        3   \n",
       "1        0        1        0        0        1   \n",
       "2        0        0        0        0        0   \n",
       "3        0        0        0        0        0   \n",
       "4        0       15        0        0       15   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((-75.41090 40.65013, -75.40742 40.651...  \n",
       "1  POLYGON ((-75.22066 40.00394, -75.21923 40.004...  \n",
       "2  POLYGON ((-79.55534 40.29320, -79.55529 40.293...  \n",
       "3  POLYGON ((-79.62842 40.30682, -79.62838 40.307...  \n",
       "4  POLYGON ((-79.88358 40.15396, -79.88353 40.154...  \n",
       "\n",
       "[5 rows x 345 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this is for working with census tracts and VTD precincts\n",
    "from shapely.geometry import Point, LineString, Polygon\n",
    "import shapely\n",
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from numpy import random\n",
    "from scipy.stats import norm\n",
    "import math\n",
    "#tractGeomFile = gpd.read_file(\"state_map_files/fl_pl2020_t.shp\")  #Boo!  Only has geometries.  do not use\n",
    "tractPopFile = gpd.read_file(\"state_map_files/pa_pl2020_t.dbf\") #for Texas, need only this file\n",
    "tractPopFile.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "a7a90b04-4b48-4ab8-9195-8d953907c3fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are 3446 popn tracts for PA\n"
     ]
    }
   ],
   "source": [
    "# EXTRACT TRACT GEOMETRIES AND POPULATIONS INTO LISTS, COMPUTE TRACT AREAS\n",
    "# If the population and geometry data are in one file, this should work.\n",
    "STATE = \"PA\"\n",
    "tractGeom = tractPopFile['geometry']  #for some states, replace with tractGeomFile\n",
    "tractPop = tractPopFile['P0010001']\n",
    "tractVAP = tractPopFile['P0030001']   #NEW 3/2/22 - USE VAP\n",
    "# tractPop2 = tractPopFile['P0020001']   #not needed; confirmed that this matches P00100001 exactly\n",
    "tractHisp = tractPopFile['P0040002']   #NEW 3/2/22 - USE VAP\n",
    "tractBlack = tractPopFile['P0030004']  #NEW 3/2/22 - USE VAP\n",
    "nTracts = len(tractPop)\n",
    "print(\"there are {0} popn tracts for {1}\".format(nTracts, STATE) )\n",
    "tractArea = [0.]*nTracts\n",
    "for t in range (0,nTracts) :\n",
    "    tractArea[t] = tractGeom[t].area\n",
    "isSkippedTract = [0] *nTracts  #this will house a temporary list of tracts for manipulation\n",
    "tractPop = tractPop.to_numpy()  #to avoid panda overwrite grousing\n",
    "tractBlack= tractBlack.to_numpy()\n",
    "tractHisp = tractHisp.to_numpy()\n",
    "tractVAP = tractVAP.to_numpy()\n",
    "stateVAP = np.sum(tractVAP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c90b3908-e2e3-46b8-98e5-ae285eca1ee3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is Hispanic population by total population PA voter-age population\n",
      "state pop= 13002700 VAP pct Hispanic is  0.06802093350028415\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our correlation of Hispanic pop to total pop?\n",
    "print(\"this is Hispanic population by total population \"+STATE,\"voter-age population\")\n",
    "print(\"state pop=\",np.sum(tractPop), \"VAP pct Hispanic is \",np.sum(tractHisp)/np.sum(tractVAP) )\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(xlabel=\"tract Voter Age Population\", ylabel=\"tract Hispanic pop\")\n",
    "x = [0,10000]\n",
    "y = [0,10000]\n",
    "plt.plot(tractVAP, tractHisp, marker='.',linestyle=\"none\")\n",
    "plt.plot(x,y,linestyle = 'dashed')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "237df16d-af87-47ba-acb1-2ef5702c1406",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is voter-age Black population by total VAP PA\n",
      "total state pop= 13002700 , VAP pct Black is  0.10336215179569361\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our correlation of Black pop to total pop?\n",
    "print(\"this is voter-age Black population by total VAP \"+STATE)\n",
    "print(\"total state pop=\",np.sum(tractPop), \", VAP pct Black is \",np.sum(tractBlack)/np.sum(tractVAP) )\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(xlabel=\"tract VAP\", ylabel=\"tract Black VAP\")\n",
    "x = [0,10000]\n",
    "y = [0,10000]\n",
    "plt.plot(tractVAP, tractBlack, marker='.',linestyle=\"none\")\n",
    "plt.plot(x,y,linestyle = 'dashed')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "10b208af-8eb0-45fa-9dc0-8873eb797200",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of Census tract population for PA\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#for curiosity, let's plot the distribution of populations among tracts\n",
    "n_bins=50\n",
    "print(\"this is a histogram of Census tract population for \"+STATE)        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractPop, bins=n_bins)\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4aac60d6-4cce-469b-b075-ad743acda38c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is population by Census tract for PA\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our distribution of tract populations by area?\n",
    "print(\"this is population by Census tract for \"+STATE)  \n",
    "plt.plot(tractPop, tractArea, marker='.',linestyle=\"none\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f159bf55-a25e-45c0-b7ea-4c21fbce9bb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'shapely.geometry.polygon.Polygon'> <class 'shapely.geometry.polygon.Polygon'>\n"
     ]
    }
   ],
   "source": [
    "print( type(tractGeom[0]),type(tractGeom[1]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "5e8f8f92-a336-43e5-8ac5-20c657cb5857",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "looking for nonPolygons in census tract data\n",
      "146 2288 0.008693617900000027 nonPolygon tract no, pop, area\n",
      "2.8233549999924065e-06\n",
      "0.008690794545000035\n",
      "569 4725 0.00028516723750002357 nonPolygon tract no, pop, area\n",
      "0.00022965236700001662\n",
      "5.551487050000694e-05\n",
      "670 4082 0.001753843940999895 nonPolygon tract no, pop, area\n",
      "4.8713435000013345e-05\n",
      "0.0017051305059998815\n",
      "689 4315 0.0009826496540000325 nonPolygon tract no, pop, area\n",
      "1.9583797000020324e-05\n",
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      "8.931376999993035e-06\n",
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      "2.950760899999571e-05\n",
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      "0.0008034840724999712\n",
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      "0.002594604985500005\n",
      "3.959173650002392e-05\n",
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      "1.5412397999986217e-05\n",
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      "0.004880593855000018\n",
      "1.249179249998457e-05\n"
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    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"looking for nonPolygons in census tract data\")\n",
    "notPoly = [0]*nTracts\n",
    "for m in range(nTracts):\n",
    "    if type(tractGeom[m]) != type(tractGeom[1]):  #assuming tract 1 is a single polygon\n",
    "        notPoly[m] = 1\n",
    "        print(m,tractPop[m], tractArea[m], \"nonPolygon tract no, pop, area\")\n",
    "        x = tractGeom[m].centroid.x\n",
    "        y = tractGeom[m].centroid.y\n",
    "        if y < 999 :  #let's zoom in on these\n",
    "            for geom in tractGeom[m].geoms :\n",
    "                xg,yg = geom.exterior.xy\n",
    "                print(geom.area)\n",
    "                plt.plot(xg,yg)\n",
    "            plt.text(x+0.0,y+0.01,m,fontsize=9)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "69edb6b2-7b88-43ca-8017-ff4d271ce3e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "80 0 ( -75.24602434321221 , 39.864641545001476 )\n",
      "292 21 ( -75.18328254782477 , 39.95905792206266 )\n",
      "295 0 ( -75.14896867486868 , 39.902180026412516 )\n",
      "554 0 ( -75.20400466730477 , 39.935830994789754 )\n",
      "555 1 ( -75.20311162153232 , 39.92614104027708 )\n",
      "556 6 ( -75.20098832986137 , 39.9347931834737 )\n",
      "557 0 ( -75.20218491782768 , 39.909755840555064 )\n",
      "558 0 ( -75.20102428935064 , 39.89840462967348 )\n",
      "789 3 ( -75.00626369085369 , 40.08665102036333 )\n",
      "1012 0 ( -75.23182123884034 , 39.8804246192709 )\n",
      "1018 2 ( -75.14390671959882 , 40.01752917498995 )\n",
      "1108 0 ( -80.16949919896159 , 42.25406940933501 )\n",
      "1156 9 ( -75.13891165543114 , 39.91602668366243 )\n",
      "1157 3 ( -75.16973587732845 , 39.889938723199606 )\n",
      "1159 4 ( -75.19737537494689 , 39.94638547786614 )\n",
      "1161 3 ( -75.19202953839184 , 39.9828289023397 )\n",
      "1171 12 ( -75.20588865334302 , 39.98874628826751 )\n",
      "1172 44 ( -75.21979980595938 , 39.89470229605235 )\n",
      "1179 65 ( -75.12222584717291 , 40.01040864186597 )\n",
      "1249 8 ( -79.9143451815809 , 40.48119413312783 )\n",
      "1250 0 ( -79.90632556198244 , 40.44203773951918 )\n",
      "1798 64 ( -75.6447017016357 , 40.01912115087974 )\n",
      "1810 336 ( -75.04808802476262 , 40.06872955162822 )\n",
      "1870 0 ( -75.35662394888564 , 40.047508281225014 )\n",
      "1878 78 ( -75.53730552979141 , 39.887726882934025 )\n",
      "1986 0 ( -75.34474100548738 , 40.1471040945896 )\n",
      "1993 11 ( -75.39441743600499 , 40.08881463786561 )\n",
      "1995 1 ( -75.44026119550476 , 40.10013326623244 )\n",
      "2136 13 ( -80.02001522046032 , 40.48194082453083 )\n",
      "2137 18 ( -79.94216236248572 , 40.434346767587996 )\n",
      "2160 0 ( -79.90572278627806 , 40.43255040363694 )\n",
      "2212 29 ( -80.0114603068859 , 40.43755314354908 )\n",
      "2238 4 ( -79.94881322205954 , 40.47310387101565 )\n",
      "2272 0 ( -80.01088014567458 , 40.44653986115801 )\n",
      "2277 3 ( -79.96550964722242 , 40.43259786009108 )\n",
      "2313 19 ( -80.02805134461846 , 40.45201153832926 )\n",
      "2339 0 ( -80.04194766624245 , 40.46659289807629 )\n",
      "2491 52 ( -75.21192955305081 , 40.04709113206455 )\n",
      "2676 0 ( -74.76464587792336 , 40.1543514662875 )\n",
      "2759 0 ( -79.89237033433233 , 40.48825827724938 )\n",
      "2768 314 ( -79.897234037389 , 40.48016283187467 )\n",
      "2906 13 ( -75.26257878911746 , 39.971590445469104 )\n",
      "3344 2 ( -75.17071478668595 , 39.90553818126993 )\n",
      "3416 301 ( -79.99811766564308 , 40.44955393159073 )\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# For potential later issues, identify and visualize low-population tracts.  #SEE FLORIDA CODE FOR PENINSULA COMPRESSION EXAMPLES\n",
    "for m in range(nTracts):\n",
    "    x = tractGeom[m].centroid.x\n",
    "    y = tractGeom[m].centroid.y\n",
    "    \n",
    "    if(notPoly[m] == 0) :\n",
    "        indent = \"indent\"\n",
    "        x2,y2 = tractGeom[m].exterior.xy  #turn these two on to show state map\n",
    "        if (x <-79.8 and y > 42) :  #e.g. to focus on Philly area.  One Lake Erie tract known (1108)\n",
    "            plt.plot(x2,y2,c=\"purple\")\n",
    "    if(tractPop[m] < 400):\n",
    "        print( m,tractPop[m],\"(\",x,\",\",y,\")\" )\n",
    "        if (x <-79.8 and y > 42) :\n",
    "            plt.text(x+0.1, y+0.1,m, fontsize=9)\n",
    "            plt.scatter(x,y)\n",
    "    else:\n",
    "        indent = \"indent\"\n",
    "        # plt.scatter(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "d63968a2-afc1-4913-863c-4c415fb9c0b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "looking for tracts with zero area\n"
     ]
    }
   ],
   "source": [
    "# For potential later issues, identify and visualize zero-Area tracts.  See Ohio code for more code\n",
    "isSkippedTract = [0]*nTracts\n",
    "print(\"looking for tracts with zero area\")\n",
    "for m in range(nTracts):\n",
    "    if(tractArea[m] == 0):       \n",
    "        x = tractGeom[m].centroid.x\n",
    "        y = tractGeom[m].centroid.y\n",
    "        print( m,tractPop[m],\"(\",x,\",\",y,\")\" )\n",
    "        x2,y2 = tractGeom[m].exterior.xy\n",
    "        plt.plot(x2,y2,c=\"purple\")\n",
    "        #print(tractGeom[m])\n",
    "        plt.scatter(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "baabb633-83ac-4315-92bd-1d6fdd9e999b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now, manually flag tracts that should be excluded from the map, such as lakeshore empty tracts\n",
    "isSkippedTract = [0]*nTracts\n",
    "skipList = [1108,2676]   #Lake Erie and northeasternmost Susquehanna.  All others are small.\n",
    "for s in skipList:\n",
    "    isSkippedTract[s] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "72abde7f-085d-4c0a-abe3-29e8d6203b40",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATEFP</th>\n",
       "      <th>COUNTYFP</th>\n",
       "      <th>VTDST</th>\n",
       "      <th>NAME</th>\n",
       "      <th>G20PREDBID</th>\n",
       "      <th>G20PRERTRU</th>\n",
       "      <th>G20PRELJOR</th>\n",
       "      <th>G20ATGDSHA</th>\n",
       "      <th>G20ATGRHEI</th>\n",
       "      <th>G20ATGLWAS</th>\n",
       "      <th>G20ATGGWEI</th>\n",
       "      <th>G20AUDDAHM</th>\n",
       "      <th>G20AUDRDEF</th>\n",
       "      <th>G20AUDLMOO</th>\n",
       "      <th>G20AUDGFAI</th>\n",
       "      <th>G20TREDTOR</th>\n",
       "      <th>G20TRERGAR</th>\n",
       "      <th>G20TRELSOL</th>\n",
       "      <th>G20TREGRUN</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>42</td>\n",
       "      <td>001</td>\n",
       "      <td>000010</td>\n",
       "      <td>ABBOTTSTOWN</td>\n",
       "      <td>137</td>\n",
       "      <td>345</td>\n",
       "      <td>5</td>\n",
       "      <td>136</td>\n",
       "      <td>332</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>124</td>\n",
       "      <td>327</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>129</td>\n",
       "      <td>332</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>POLYGON Z ((-76.99801 39.88359 0.00000, -76.99...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>42</td>\n",
       "      <td>001</td>\n",
       "      <td>000020</td>\n",
       "      <td>ARENDTSVILLE</td>\n",
       "      <td>175</td>\n",
       "      <td>275</td>\n",
       "      <td>6</td>\n",
       "      <td>171</td>\n",
       "      <td>262</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>129</td>\n",
       "      <td>297</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>150</td>\n",
       "      <td>278</td>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "      <td>POLYGON Z ((-77.31141 39.92625 0.00000, -77.30...</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>42</td>\n",
       "      <td>001</td>\n",
       "      <td>000030</td>\n",
       "      <td>BENDERSVILLE</td>\n",
       "      <td>102</td>\n",
       "      <td>202</td>\n",
       "      <td>8</td>\n",
       "      <td>106</td>\n",
       "      <td>189</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>96</td>\n",
       "      <td>190</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>101</td>\n",
       "      <td>191</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>POLYGON Z ((-77.25596 39.98075 0.00000, -77.25...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>42</td>\n",
       "      <td>001</td>\n",
       "      <td>000040</td>\n",
       "      <td>BERWICK</td>\n",
       "      <td>374</td>\n",
       "      <td>954</td>\n",
       "      <td>13</td>\n",
       "      <td>362</td>\n",
       "      <td>934</td>\n",
       "      <td>19</td>\n",
       "      <td>12</td>\n",
       "      <td>326</td>\n",
       "      <td>942</td>\n",
       "      <td>41</td>\n",
       "      <td>13</td>\n",
       "      <td>348</td>\n",
       "      <td>924</td>\n",
       "      <td>33</td>\n",
       "      <td>19</td>\n",
       "      <td>MULTIPOLYGON Z (((-76.99558 39.88677 0.00000, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>42</td>\n",
       "      <td>001</td>\n",
       "      <td>000050</td>\n",
       "      <td>BIGLERVILLE</td>\n",
       "      <td>193</td>\n",
       "      <td>366</td>\n",
       "      <td>7</td>\n",
       "      <td>196</td>\n",
       "      <td>350</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>182</td>\n",
       "      <td>357</td>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "      <td>181</td>\n",
       "      <td>357</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>POLYGON Z ((-77.25594 39.93043 0.00000, -77.25...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "  STATEFP COUNTYFP   VTDST          NAME  G20PREDBID  G20PRERTRU  G20PRELJOR  \\\n",
       "0      42      001  000010   ABBOTTSTOWN         137         345           5   \n",
       "1      42      001  000020  ARENDTSVILLE         175         275           6   \n",
       "2      42      001  000030  BENDERSVILLE         102         202           8   \n",
       "3      42      001  000040       BERWICK         374         954          13   \n",
       "4      42      001  000050   BIGLERVILLE         193         366           7   \n",
       "\n",
       "   G20ATGDSHA  G20ATGRHEI  G20ATGLWAS  G20ATGGWEI  G20AUDDAHM  G20AUDRDEF  \\\n",
       "0         136         332           7           7         124         327   \n",
       "1         171         262          13           3         129         297   \n",
       "2         106         189           7           5          96         190   \n",
       "3         362         934          19          12         326         942   \n",
       "4         196         350           9           7         182         357   \n",
       "\n",
       "   G20AUDLMOO  G20AUDGFAI  G20TREDTOR  G20TRERGAR  G20TRELSOL  G20TREGRUN  \\\n",
       "0          20           8         129         332          11           9   \n",
       "1          20           2         150         278          14           6   \n",
       "2          15           5         101         191          11           5   \n",
       "3          41          13         348         924          33          19   \n",
       "4          16           5         181         357          14           8   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON Z ((-76.99801 39.88359 0.00000, -76.99...  \n",
       "1  POLYGON Z ((-77.31141 39.92625 0.00000, -77.30...  \n",
       "2  POLYGON Z ((-77.25596 39.98075 0.00000, -77.25...  \n",
       "3  MULTIPOLYGON Z (((-76.99558 39.88677 0.00000, ...  \n",
       "4  POLYGON Z ((-77.25594 39.93043 0.00000, -77.25...  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now, let's read in the voting data  \n",
    "VTDdbf = gpd.read_file(\"state_map_files/pa_vest_20.dbf\")\n",
    "VTDdbf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4f3b9ddb-b971-472c-a353-521377f95e46",
   "metadata": {},
   "outputs": [],
   "source": [
    "#VTDdbf = VTDdbf.to_crs(tractPopFile.crs)  #MD's precinct file in wrong CRS\n",
    "#VTDdbf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "3fe4ceba-8077-4485-ad0a-f8f2a5755040",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9150 0.4940024860661418 6838917 = number of precincts, statewide GOP vote, total Trump+Biden votes\n"
     ]
    }
   ],
   "source": [
    "# Pull VTD geopandas data columns into arrays\n",
    "vtdGeom = VTDdbf['geometry'] \n",
    "# vtdGeom = VTDdbf['geometry'] #can't use VTDdbf because it uses alternate coordinate system\n",
    "vtdTrump = VTDdbf['G20PRERTRU']\n",
    "vtdBiden = VTDdbf['G20PREDBID']\n",
    "\n",
    "nPrecincts = len(vtdGeom)\n",
    "stateGOP = np.sum(vtdTrump)/(np.sum(vtdTrump) + np.sum(vtdBiden) ) \n",
    "print(nPrecincts, stateGOP, np.sum(vtdTrump) + np.sum(vtdBiden),\n",
    "      \"= number of precincts, statewide GOP vote, total Trump+Biden votes\" )\n",
    "vtdTrump = vtdTrump.to_numpy()  #these two lines try to avoid pandas complaints when we overwrite precinct data\n",
    "vtdBiden = vtdBiden.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "c5c57f52-b8d2-4ef4-9d02-2429ed5e59da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#see NJ code for convex-hulling tracts\n",
    "#ID the non-polygon PRECINCTS\n",
    "notPolyVTD = [0]*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    if type(vtdGeom[p]) != type(vtdGeom[0]):\n",
    "        notPolyVTD[p] = 1\n",
    "        for geom in vtdGeom[p].geoms:    \n",
    "            xs, ys = geom.exterior.xy\n",
    "            plt.plot(xs,ys)\n",
    "        #plt.text(vtdGeom[p].centroid.x, vtdGeom[p].centroid.y,p)\n",
    "    else:\n",
    "        hi = \"hi\"\n",
    "        #x,y = vtdGeom[p].exterior.xy\n",
    "        #plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "ea498c6a-d054-409e-92fb-cda921e2911f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#prep for geography triage\n",
    "#isSkippedTract = [0]*nTracts  #done above; already skipped two lakeshore tracts\n",
    "isSkippedPrecinct = [0]*nPrecincts  #will be used later"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "3ded1b1f-5629-4d8f-826b-e037e5dcb3da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on tract 200\n",
      "working on tract 400\n",
      "working on tract 600\n",
      "working on tract 800\n",
      "working on tract 1000\n",
      "working on tract 1200\n",
      "working on tract 1400\n",
      "working on tract 1600\n",
      "working on tract 1800\n",
      "working on tract 2000\n",
      "working on tract 2200\n",
      "working on tract 2400\n",
      "working on tract 2600\n",
      "working on tract 2800\n",
      "working on tract 3000\n",
      "working on tract 3200\n",
      "working on tract 3400\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#build basic PA map\n",
    "#tractMAP = tractGeom[0] #uncomment for first time thru loop  **************\n",
    "for t in range(1,nTracts):\n",
    "    if t%200 == 0:\n",
    "        print(\"working on tract\",t)\n",
    "    if isSkippedTract[t] == 0:\n",
    "        uncomment = \"if-redone\"\n",
    "        # tractMAP = tractMAP.union(tractGeom[t])  #uncomment to build map  ********\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y)\n",
    "pt1 = Point(-74.93,40.4)\n",
    "pt2 = Point(-74.93,40.0)\n",
    "pt3 = Point(-74.6,40.0)\n",
    "pt4 = Point(-74.6,40.4)\n",
    "clipPoly = Polygon([pt1,pt2,pt3,pt4])  #clip eastern outcropping NE of Philly\n",
    "xc,yc = clipPoly.exterior.xy\n",
    "plt.plot(xc,yc)\n",
    "\n",
    "plt.show()\n",
    "#wholeMAP = tractMAP  #in case we later slice parts out\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "f33de347-5473-4b11-b3ae-86691b33098d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have flagged  51 tracts w pop 223142.0  to reassign to tracts...\n",
      "[182, 204, 437, 834, 835, 1607, 1693, 1696, 1725, 2018, 2749, 3442, 3443]\n",
      "I have flagged  115 precincts to reassign to precincts...\n",
      "[919, 923, 924, 926, 990, 1006, 1020, 1024, 1031, 1040, 1086, 5777, 6464, 6465, 6495, 6496, 6525, 6526, 6527, 6539, 6540, 6541, 6542, 6543, 6545, 6546, 6550, 6560]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#find all tracts and precincts centered in this clipPoly, flag for cutting\n",
    "# and, ID all nearby tracts and precincts to receive their pops and voters\n",
    "\n",
    "isSkippedTract = [0]*nTracts\n",
    "isSkippedTract[1108] = 1\n",
    "isSkippedTract[2676] = 1\n",
    "#skipList = [1108,2676]   #Lake Erie and northeasternmost Susquehanna. \n",
    "#for s in skipList:\n",
    "#    isSkippedTract[s] = 1\n",
    "cutTractList = [-99999]\n",
    "cutPrecinctList = [-99999]\n",
    "tractReceivers = [-88888]\n",
    "precinctReceivers = [-88888]\n",
    "popToCut = 0.\n",
    "for t in range(nTracts):\n",
    "    x = tractGeom[t].centroid.x\n",
    "    y = tractGeom[t].centroid.y\n",
    "    if tractGeom[t].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedTract[t] = 1\n",
    "            popToCut += tractPop[t]\n",
    "            if cutTractList == [-99999]:\n",
    "                cutTractList = [t]\n",
    "            else:\n",
    "                cutTractList.append(t)\n",
    "            x,y = tractGeom[t].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        if tractGeom[t].distance(clipPoly) < 0.03 :  # and tractGeom[t].centroid.x > -76.4 :\n",
    "            if tractReceivers == [-88888]:\n",
    "                tractReceivers = [t]\n",
    "            else:\n",
    "                tractReceivers.append(t)\n",
    "xp,yp = clipPoly.exterior.xy\n",
    "plt.plot(xp,yp)\n",
    "\n",
    "for p in range(nPrecincts):\n",
    "    x = vtdGeom[p].centroid.x\n",
    "    y = vtdGeom[p].centroid.y\n",
    "    if vtdGeom[p].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedPrecinct[p] = 1\n",
    "            if cutPrecinctList == [-99999]:\n",
    "                cutPrecinctList = [p]\n",
    "            else:\n",
    "                cutPrecinctList.append(p)\n",
    "    else:\n",
    "        if vtdGeom[p].distance(clipPoly) < 0.03 : # and vtdGeom[p].centroid.x > -76.4 :\n",
    "            if precinctReceivers == [-88888]:\n",
    "                precinctReceivers = [p]\n",
    "            else:\n",
    "                precinctReceivers.append(p)\n",
    "print(\"I have flagged \",len(cutTractList),\"tracts w pop\",popToCut,\" to reassign to tracts...\")\n",
    "print(tractReceivers)\n",
    "print(\"I have flagged \",len(cutPrecinctList),\"precincts to reassign to precincts...\")\n",
    "print(precinctReceivers)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "33897c0d-3254-4a76-9b82-314a8f320eaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 2 of 4\n",
    "#visualize the to-be-cut precincts, just to confirm there are really xx precincts up there\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] ==1:\n",
    "        if notPolyVTD[p]==1:\n",
    "            for geom in vtdGeom[p].geoms:\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y)\n",
    "        else:\n",
    "            x,y = vtdGeom[p].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "e2595c62-579a-4af1-9552-0859524782be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 3 of 4\n",
    "#now visualize the receivers\n",
    "for pp in range(len(precinctReceivers)):\n",
    "    p = precinctReceivers[pp]\n",
    "    if notPolyVTD[p]==1:\n",
    "        for geom in vtdGeom[p].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = vtdGeom[p].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "9e1d7fcd-cdeb-4317-9f89-5291a9595587",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now visualize the TRACT receivers\n",
    "for tt in range(len(tractReceivers)):\n",
    "    t = tractReceivers[tt]\n",
    "    if notPoly[t]==1:\n",
    "        for geom in tractGeom[t].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = tractGeom[t].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "d2622568-1fc9-4089-9ae4-2550ea6fbaaf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have finished slicing  223142.0 people (VAP of 177203.0 ) and  128509.0 voters out.\n"
     ]
    }
   ],
   "source": [
    "#now, cut and redistribute\n",
    "cutPop = 0.\n",
    "cutVAP = 0.\n",
    "cutHisp = 0.\n",
    "cutBlack = 0.\n",
    "cutTrump = 0.\n",
    "cutBiden = 0.\n",
    "\n",
    "for ct in range(len(cutTractList)):\n",
    "    t = cutTractList[ct]\n",
    "    cutPop += tractPop[t]  #sum up the cut pops\n",
    "    cutVAP += tractVAP[t]\n",
    "    cutHisp += tractHisp[t]\n",
    "    cutBlack += tractBlack[t]\n",
    "    # Now we zero out the pops in the cut tracts\n",
    "    tractPop[t] = 0\n",
    "    tractVAP[t] = 0\n",
    "    tractHisp[t] = 0\n",
    "    tractBlack[t] = 0\n",
    "# Now distribute the pops among the receivers\n",
    "ntR = len(tractReceivers)\n",
    "NTR = float(ntR)\n",
    "for rt in range(ntR) :\n",
    "    t = tractReceivers[rt]\n",
    "    tractPop[t] += cutPop/NTR\n",
    "    tractVAP[t] += cutVAP/NTR\n",
    "    tractHisp[t] += cutHisp/NTR\n",
    "    tractBlack[t] += cutBlack/NTR\n",
    "\n",
    "#             now do the same for the precincts\n",
    "for cp in range(len(cutPrecinctList)):\n",
    "    p = cutPrecinctList[cp]\n",
    "    cutTrump += vtdTrump[p]\n",
    "    cutBiden += vtdBiden[p]\n",
    "        # Now we zero out the pops in the cut precincts\n",
    "    vtdTrump[p] = 0\n",
    "    vtdBiden[p] = 0\n",
    "\n",
    "# Now distribute the pops among the receivers\n",
    "npR = len(precinctReceivers)\n",
    "NPR = float(npR)\n",
    "for rp in range(npR) :\n",
    "    p = precinctReceivers[rp]\n",
    "    vtdTrump[p] += cutTrump/NPR\n",
    "    vtdBiden[p] += cutBiden/NPR\n",
    "print(\"I have finished slicing \",cutPop,\"people (VAP of\",cutVAP,\") and \",cutBiden+cutTrump,\"voters out.\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "fe47f683-719d-4a02-9648-905bddbf28a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Now, repeat for western MD\n",
    "\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y)\n",
    "pt1 = Point(-80,40)\n",
    "pt2 = Point(-80,39)\n",
    "pt3 = Point(-78,39)\n",
    "pt4 = Point(-77.5,40)\n",
    "clipPoly = Polygon([pt1,pt2,pt3,pt4])  #LEFTOVER FROM INDIANA. plan to clip off SW corner near Evansville\n",
    "xc,yc = clipPoly.exterior.xy\n",
    "plt.plot(xc,yc)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "cac3531a-35c7-438d-b5b9-5fcdf0745329",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have flagged  149 tracts w pop 201388.0  to reassign to tracts...\n",
      "[557, 558, 564, 565, 566, 568, 569, 570, 573, 575, 576, 585, 664, 668, 818, 825, 826, 1524, 1525, 2522, 2524, 2528, 2981, 2984]\n",
      "I have flagged  92 precincts to reassign to precincts...\n",
      "[649, 650, 652, 658, 664, 670, 672, 673, 675, 676, 681, 690]\n"
     ]
    },
    {
     "data": {
      "image/png": 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70TbbgAr0PuPorFzl5eWntX31L89ix0Kbac80+nrvAcPR37OYAmMcA9b8lvVfvnTaZT2ey+ki/tAuinsOQKc7819H94L55IbG0ndMCll52mdSavpF+5m/ACklG5ZNAS/0CnwRUZuPJCNtIwjBFY8/02g5ls/6GtDR87KxBBbFYO5dTfEH22jjiMQ8qQ0m84k9LaSULMkqJV6vJy723Ca4ev+XjTikgTtHdjrlul3bduX6iOvReXVcN/c6jhQfOen6m/et5oClkCqzC4vFv255dcZaYlc/Suy+99G92ZOcx7sQ483E5Wdpcl/2wgK+ve9OanSCwWMuQ29oeaNCqU07rwFBofWWV7uq+eWwdq7v6X1P3fJhffqj719CQGZbUpetqbfNoW1FzHttMxnPr2Pvc2up+NpK4TptsJPXqSfnuwnY/Nwc0m9n2YaRfLV0JLEymzxdR6IC4nl46cN8cHgHFp1gZHhgi4/lfFGB3kccDfB2u73F2+Zt/oW4ynTy288gMCK2yfWi2iYR/8flbPcfTMquf7Jvw/yT7rfE7qK0xlX3XErJnD15fLojhyWHi/F4vezauBOLx0lo/74tLndDuXsPkpCzj4oR4/C4nHyYn8S0qHyG9P4aJBySW1iytBOuMDuBJfEY40fxhz9/RM9H5vBp+Eiq/UMJ7dn7hP1WlVeQu3ctwdG92VepBcVIfwgt0Zp8ojebcFeeMMEaq1dmstnh4OoO53bGNK/Xy7fpBYQa3Ewf2q1Z2zw99Wmuj76ecl05i/YuOum66/Yda4Y5/iavK/27usc1xkji/QtJGFOCt4kvtbLdu/ni/rs44rLTu3N3et//u2aVtaGqGm0K60BL/fcx6U11j93e+lcVt99yBeVBBez8poSsvGNXMcs/34t/VgWWChcBdm2bdmUdqClJpLoqjSp/Axa7h7jQP7KV/nikm0O0pzLiTt4Z/w4mvZlFxVW0N5RfVEnMGlKB3ke0b98egLi4uBZtJ6XEufDv1GAh7uq/nXJ9k38Q7e6ejQsDbX+6A3t1VaPrzfhiE/3+toC+zy2g499T6fnqErr8YyGPfryFp2elcde76+jx2lLWvfMRAF1GprSo3I3Z9snXAPS87Tr0Oj1edLQxuwiI7M/YUbuo3hHCO2l38cjiv/P6pl9xy4s/Mc8bTYR0sCegMzvjhvLR21rm7ZzDh0j95ivcbhfLZ30L0sngq67i5w1LAYjbq6Na5wDAmVVBwUsb8brq37j7emM2AHuKKs/42E4m/UA2WQ4/DDpadFXUNrQtAHP3zT3pereM0mrHQ7zd6nUdrIk7dk8iwH1snue4GbfjaXBlWbp7F7OfeoQyHYy//Cou+8fLzS7n8Rau/YZN7/wXAD+jf73XDDoDNyTfAMCnuz6t91qQfyCT7u6FzmPgy5fWsWnHDjxuL9U2Fwc9x26Em3oKzEYzsTs6YQi1UhkcjsegY6EtmlfFX/jW+CRPiVcZ3nYoSSFJ3DXgH3gNkXQ2XtzzXKtAf4nL+uVNEmt2Utz1VkzBkc3aJiIikgxzJ5wYWJpXP9B/f6CQwTNXsTmtgKj4IAb1bUNkpD8eKTH46bliXBIPzujOhBEJWKz5jNqizRW/8ZuX2Lb5F9yek7fvArhq7Gz84m1Sbx5P6rSh7E9bBoBp2UIy23SkU59kds97AS867FU2dqRvZu/iRfz14CPsyO9BmSeE9bpodvnHcEdAKVOHae3nUaKK3PIKtq1ZyQcffMDa7btYNX8++9YtxOQfT69xA5g+fjyRBkGnmnjyBtegD9FqkdLlxZVXP6A/cEUyAAvLKtmRXtCsz/Z0XPffNACsTgMO16k/v6N+PPAjACWekpOu993q2QBc2e3qumXS62XpT0vYYWtL1RUz4fbvcAX3oTLPTOlLr7J/7Dhy//IXbKkL8DoczH/9n9QYdMx44I/0/dXdLTxCzf4D29j61oe4zAJHjwj69D7x5nd8YDwAOnFiaKssX8KWnh8g3HrW/Sufdx9chvRK2nYOxXRzV7xSYs23oA8uItw7DQC70Y5bD++429LBWcQahz8hspDBUUn8vCedFzflg5T8pceE0zqm80X1uvERpzNIoyxrJ9EbXqDQlECbGS+cdN2s3L3k7viJiKRBlGdvo79zD+8xlZdnbeGmUe0J8zfx0/Y8DmWUInSCvj2j+PzGAfgZmr6cfSJ4NwfXQZsyQZc5m2HOZrabwBplweSBqgA93gALHiC8oBqLrYYas46Aag+BTnAHCsxOyZE//gHP396nbUkOh27WenDED7kW/ZYDfFrag08/y9fe0BLKb9vlcs9N19P35VUA/Ormsdw5az2hBh2/u2Eyn339DfMWLAaD1k5rPVCAx1lC7wkzEELQP7knuaZ9VOFkyKQJmKdbyH1hPV6bE2Ob+l3runSN4u/9E3l6SyZzVxyiZ++YFp+j5ugU7GW3TU+gzoXZeOo/aWuFlYLyAvbJfQD46f1Oun7qoflYzHomD5xRt2z/xrXk7N1Lz/veImDgZQAY/7gC8vJIzM2l7Os5VKQuoPybucjwMHLbhQOQMGZ8i49v8bp5rPz+M8yZVegEXPPcC3RP7NvouhllGQD0iOxRb7mrupjHd86kKkCwp8+L9M0dT5887Qb6kGFOYnpHsWP+YYKLqjHcNBzXnFQApM4LQlCFkTK91gQnC19lzIc/cXj/WBxD4jHYnCQGXLw3YkEFep8j5Sk6CNfyuF1Uz74df7yYb/ofemPTw7ZlVTER/x1Dgqcaau9l7QnrRcy45+CrfXyWqv1xSQE9ekUxc2ov4kNOHjwA9rsy+PEuE+tvWoMzN4edP35K6Z7tmHOs2AyS4FInwlah9WEPNyN6xKOrdlDubyFw3BiGXnkvaz97ndiXZlP48H2ECx2pUT2Y7PUSktCDOTPKWL/nEHuOWPnW1hmAveWS0PAQ7g0updIlKXZ7OGw3cU0XP7r07I3u00/x+mkB2+R1k7VuISb/OEbfcoX2ua2voY1RYI2xYDKbWTJ/Hza9hxQAw4lfttNGJfH0lkxCQs7dsPjPfj+Z/v9YxPC4U2dMtNltXD3nasp0ZXXLLou5rMn1y8us7DHnMEDfjV2LFuJ2OXHa7excvpiw2Di6jx5Xb31jbCzG2Fj8BwxAev5B1Zo1lMydizFrDy6DnrL8PELbNH0fqKH/e/shnGsysOjB0z6MgZOvbjLIA2RVZAHQPbx7veUZGb9QpRPcHdqLlPiRpEWtpGt/DyM2fkThxpth4GDa39Gdwtc3UzDvAB0eHsf+9OfQC388VNOdw0TJA0TKAhbVpFHkjiKszQDygpPoXtG8v7kLSQV6H9HSGv3B2Y/Q2ZFB9oAniG9/wkyR9ezd/gtdPdX8POx5Qq27sSeOYPSQ6+mq1zOsYzyb8m1UOdy0D/VnUGzzazbZZTswWpIIMAUQkNSFEQ/+vUXHADDy9idYO/NLokqr2BidzHeZNYT+uIvnpvWk38DhdOs7hAfe+RZsEE0p943pCMCTf74VgFvf/Bk9koeu6A/A4IEDyThwgCK7A1lqRXqrmXTfU+gNBvbP2YffpgI2hRt4qpuF0iVpeMwCBvmTegDaHlcur8fLe7O2sd2qNecknMNeN2kHcpEIwo2uU66bkZtRL8ibPCbuG3Vfk+v/uOlrXAZJ5G4Hi3/5T91yo8WPqQ/9CZ2u6Ss2odcTOHIkgSNHcuvePcx+4WnmvvQcA6+8hh6jx6M7xc3LrdtX4ll1AGdiAPc++gZR0ae+/1RYXYhO6Agw1b+6atd+PG02v8QHZdvJd5Tx/IzvKV69ALPuHUwdBgIQEBOAMzmc0H2lZP2ylXLXEEKDNxL2jYFZL4bw1c4P8dfBIm8go2Kn0qvnRP52OJ8RQRfnIKnjqUDvS5pZscjdPJ8Ohz4lN2QA8VOfOOm6Xq8Xue4/FJkiGDP2XvyN9WuNbfzNTD2NXiWbivZTVbWHjm1OHJjUEnq9AT0GwE3E9Csxler4bH0Wz1zRnX/O38PHqw/j9lrQIZl9/2g6Jx7re30ov5Q1eV4GRbhJjAkDYNK11+P3r3+xxO5AGk1kdJqAKSycqjI7Wzbm0M1g4PleFhCCu91mfsRJjgmCru9S70boNwszeGm31oe+s8HA6JSW3SRvLofLw4J0rRbr0TfdrRHA7XHTt31fklYlcZjDzBw2k6Gdmx785qyx8/X6TzDF6OimT6IisIDbX34bv+AQ9AZDiyoX4cldufx3j5L6n/9jwXtvcWRnOpc/+MgJ+yjPzmPrZ98R37sXi9JnIZD86pEXmxXkoXZ0dCMCg2L57oZlvP/DHXxgz8T1+TgeLO9BhNQRNmRi3Xodb+3G/mfXsjfdTV7FrzHWhBK7dQmF9/yZ2D9I9rt0vDLsfS5PTuH6tAxElYuvNx7iN/3bEduMq9gLRQV6H1Fd5iaycDi/vL2HHmPKGDa5JwajgQpbJd++u4aaCg/RSUGMGx+M7puncZtNRPxqdr08Io0pLNhPt7IdrBn8Z4YZz95kCk9ueBfQ86e+vzrjfaX19jBmOcQPbMc1RW34YuMRHv9mO3O2ZKPXCW4a1I57R3UkKbJ+zeuxT1cCgj9MOdal8sCmTSyxWonOL8BV7eDA9GF8P/trtnrbsVRYGJAcQbZF8FnbtoztGkNN6k4+wUVCYP0/8h+35OAP/HjHYJI6h6MznJt+D+Ne+JEcu/ZnfLis6Rr9rLRZvLSt/tiHlQdXnjTQ11RUkB9RQ1urhfG33cMXf32cdXO/4LLfPHhaZe2UMpiOH3zG2jmfs3bOZ8R26Uq/SVPxujz88Je/U2zNpsZZid1VyebNP6IDKiP1xMd0aPZ7xAbEkl2RTWlNKWEW7ct7a/r/mL/nK7oExDMycRyOjJ/41FNIO/dP3KrvSkRoeN32BpMey+QEDs38HKGPpCB0KJ1Ygi6zmrjX2rPl8gGMmdgHm9vD2rIqdIU1VDjcLNtbxE2DEk7rczkfVKD3EUGWMIQ8grdKz/afSkhPXUSPcdHs3ZyDpzgAr8FDdpGT/220Ai8xOHElKWGnbistLDhAG8AvqstZK+vqwgzyipcSEtSb4dGnHuBzMi5XNZ8PdnOgl4Nb1r7GvXfPZs7+r5izRQtgz13Zg1uHnjgqddv+LLYUC0bH6RncQ2vO8bjdLJg7F7PXy8iVK5n14KN8uLEGGEAfYz6LR0SzOsDIr7Ewtqt2Y3WucBLkAh2So3lxpJRsttkZHRJAh27N68nUUku3Z/Lol5spdh9r+z9Y1niPGyllvSAf4AkgwZDAlb2uPOl7BEdFEyoDqQpyERAahsFkorzwzHoPCSEYOuNG8g/sY9knHxCVkMSWT+eRkblJe09LJOOvu5c1WSspXLOO7Pimc800JsGcxEaxkRee+5To6gQ8FgclopxtbY18FrIarKvr1t1hNuEIOvH3eveOVXjdmeDOxFndDVtQEsEVh6GsHIPNwtOvvcuwu27BJSWmwhoAbkg5cZTuxUQFeh9hqO3dcsWDvcg6fIT0xbnsWlAGBJA0xJ/Lbx/D528somy/dsrXZ44kfvlC2oxu+kYcQM0Wbbi82T/srJV1U/FhhHRxTefrznhf67Z+QKlez+RqB1GOLGZ89yB6v2DcZdrra5dkck3ftvj71b8a+WjZbrwI/jxjcN2yxf/6FwUmEykbN7Lnnvtwtisis6gTidWSjq423HPExb+6GglopyUvq3J5qDAK2tTUbzNbtCaLSiSDE8M5FyrsDh74fBvVXi3IG/HQJ0rQsYmRmUII/j383/x29W+1cuurGBI3hG5tTz24akzbUcwu+4Gn/3kzyfpoRt78qzMuv9DpmPLgo3z21CN8+ddjTYc3PPw88UP7AJDMWEb4D6VG52Tr3g30Sz4xPcbeXSt5c/Hf2WTOw+KCEJeRnCA3GMAvXAfhVVApSSzoQfdqEyN+14c8RwYHi3ZQZs3kmiOpLLBEsGvxXqJMBjoEWgisKObw6o8x+bXD4B9OXMoPWC/Lx5qu5zuTP5lRa4mojuPBJQMg3IwoczKua7SWEfMipvrR+wztF02v1zNqan9+8+JEOo72o+eUUKbcMRidTscND41jwn0d+M3LAzCLKtb/lI10e06+23Dtsrn7V9NZt/ids1LSSJMWoPwbDHg5HT/vn0ewV9LGnUCpPphq/U6cSUk4hkThibLwU0UFd755bNh7ia2SorIKlh2uJt7PQ+e2EQDkbN/OuuJi4o8cwVjjImnyAG7aH0JitcRdchCACXu0n58eyafMXsP6DG2QzAuREfXa5x/8bgc6CddMOXtXQcd77otVVHv1dA1yclUnE/tfnMacR67kpTub/tIe2Wkk227bRke0q5fNeZub9V63jtG6q2Z28nD7y28T077jmR8AYAkM5KpHn657PuN3z9UF+aM+n/w5od4gHl31GJWVFXXLS0vy+PMbU7l+/f1sMucxuiaRwaIDkRU6OuR7uTmrL3977EEee+wWnnjuVkYMtlLl7Mzi10uxLJfcMfZFhkXcRju3h6WJHfifzs5r7goeKCsi7Kub+G2X9dybOI/Y8AN8VDGEiH02nugiWJtUTG5ALgeDcvFGWvArdRJkNvDurQPOymdyLqkavY9w1tS/bDebTUy+qX77q8FgILlvEgD9+1WxdksiuZu2ETekf5P7HTT97xwZcCO2uffTf9UzHEgeR8f45g2zb0q1R/tyCdCfWT2jurqYxc4iplja4vQLpmPZKiAKry4IGWjC1T8CtpeyPreSDetW8+aifayujK7d2sT9KVqzirO8nG8++gijycSATZvZcE8vDDm3Qh+QC/9L9fp/YYzowk8DkoGelKLjT2s209ug7atHklZzry6tYdPM7Txgs1Cqk8z9+QBXXtGRiJCT3yRticyCEr7bW0WPEC8/PnFVi26IHjp0iPGh4zlQdoB9zn3kWHOIi2z6JqfD4+Dx1U9gMVh48aZ3CY6MbnLd0xEeH8/I3jch8x1Eh5zYvt0uLoknu/2Jh/c/zqr0pUweNo38vAzunnMDR0IdTK3pykPT/kl0nNZ1dusdj2DctJguq/5Vbz8ZkX1xGzLBbWbz9ihSvF6qc7/HKQy8Pv4aDMYIMkqr2bjzFwZGZFPtGY7NfRspMp6UPHjb8wr3rQ3Fz7aGThmzyWobxKMP6fjPhO5Mvuni7j9/lKrR+4iaSu1GXGBY8/prRydoNw/tFSfmaKlHCNq160nsrbORCKzL/+9MiglAqVPLxxNuOrMa/fJNb2PXCa7oegNtJj1CiLCz/vARLObeJNrzGVzgRleovdf135bVBfkEXSk99XlY0xbzySef8P6TT1ISHMyAzZsxmmvo3EG7Agg5MgZP0UGEoxJ37hYKIgp5fPWPxNRUscBt4IU8K7q8aka/tpykJ37irT+vZvuhCvx0gmB0VK0o4MMnV7PvUNkZHefx3vo5DTc6nr26X4uCvM1m49NPP6V4azE9SnpQo6/h7Q1v4/Q0ff7f2/Ye6UXpPD/8ebpHdm9yvTOR8tiNtO8+gJLP9+I4eGJCvsF9RqKTOtKObCHzUDp3fHMd+RYn/xf7e56/f05dkAegogyHJRRT2LHgm19Yxa5F2RjceiRuevrP5+BfxzHw0LekJUwmIrwtIUFmOoW4+M26xyl2/YUS15O4ZXzdPkYM/Ax/HdiDhxFpg9W9U7BIyajwc5uo7mxSgd5HHB0npW9m7w6nU1tvy88ZzVo/PCKBrKAOGMszT6t8R3m9XpZkLUCip3vomfVS+PnwAqI9kgG9b6dt96G4jMHsCexKuTGErJowtqUVINzaB/PXnlbSHhvImtC/8p3pSa6KzENKL4cOHaIoOhqj00lYRDYHn4qjWlgwVcYRYrsVWa3dJLT5Qd8Cf8pdLnrmHcJb6MSUVoIpvbSuPNU6ScdYf3799hgeensMYaPb4O8VrF6dfUbHeZSUkiUHKki0OBjUtXlpj6urq0lLS8Nms2EwGPD39+fe/veSJJL4qegnpsydwhd7vjghCVi5o5zP9nzGpKRJTEya2MTez5zOpCfyzh7og4yUpx4+YcBfYEAQfUQ3Ztd8w81L7qHMLHm70xOMnXzPCfuSNTVI47Grp/2HyvjmmfWEaymJsOv1ZBqy6ajbir/HTk38sbkPjK/eSb7jXWq89a+CrSbJB+Hj8DMVEVJ+AIDVfQYwOjIY/zO8Ij2fVNONjzj6ByKaeVOo/dQr4OcluGXzukxWV5eRWHGA9XHDzqiME356iKKSZXSMvZoOwaefEqCs9BCrvDZuDeqETq8dg/EvR+i2+xd65lgZGlzO14F+ROqcfHvPUCIjtfeqHP4wYYt/yxX929Fm1B1YrVYcDge70t7ENsGJwxBGuPswr5dZmX17bzLf+S0ek4lAh5uxv2j5zL/qMpYN3bUbspEGM3luN//olcjE7tFE9zvWvNG3XzRLl+fj9bSs50hTlu/IpNRt4LqewSe85na7ycnJISoqCn9/7UrJ4/HwwQcfUFJSgl6vx2Qy4XQ6GTt6LGPkGNbmreW9be/x/Prn+WLPF/xp4J8YVnt+Uw+nUuWq4tc9f31Wyn4yOouBoLHtKPv2ADU7i/HrWb+n0tXtr2broZ0EewJ5ruIuBo29pdH9eL0SnTz2hTXng3SCgRKdl3CvDp2Eb6zDKWvrxwOG73nSmcibO3bTNSGRMtexm8KlunL0Qk+wJ5CZHQTr9cMYZVzKkEPL2NK3H4XhkUyMOPEcXMxUoPcVtRWh5l7NCyEwCBfhQRWnXhlI2/gNw6SbsB5TT6t4Hq+He1e/TVHJMrq0vYrPxz17Wvs5asHGt3ALwRW976y33K/b5SyqvYXw90byTDl2peJFENH3coQQREVpg73Stm1B54gEPwteFxj8u1H5vpZZU+904jdkCPZ16wCYnrGSj7pN4f+mt+fv32cRrtdz6y0nzh61ebPWFbFL14gzOtajZq3cgx4vd4yrf9PS7XbzwQcfkJ+fj9FoZNCgQQwfPpyamhpKSkro168fLpeLHTt2kJKiZQkVQjCs7TCGxg5l6ZGlvLrpVe5ddC8D2wxkQMwAFmVqqYs7hZ1Z99fmChgYS9W6PMp+PoSlazjiuCvTQYsP8Q/Dg3Sv7kC73/TQMq4ePIghIgJ9aCgA1fklWLJ24ugzum47fZmbcjM8/eYEXA439//mDba07c4WdxeOhI2l2BvG9PwqnlmygCvQzlGlrYDNxfN4e/JurssfxGE5hjtWH8RTDWl9H2Lj2CyQkjHBZ96R4HxqPdceyknV1eibH+kJsxRzoKgD79y3hG8f/ZCsxcvx2KwnrOr1eojZ9G8yA5Lo0b3xmZROptzpYOJPD7H+4H8JDh7Ep2P+jOkMc3f/nLuSDh5BcueT9wU/npSSsILVFPh1wRRy7GrCbq/AbM7CZOpHTEhPdAJKK/YQdvvtx9ZZt47Qm24kbP4iHp/0GNemtOOqYT3Q6wSmRkZjOp0eslfnU6z30qfnmeejd3u8rMl2kBzkIi66frfNvLw88vPzGTx4MN26dWP16tW8/PLLrFu3jsjISDIzM5k+fTpPP/00U6fW/6IWQjAuYRzfTv+WPw74I8X2YmamzySjLIMBMQMw6s7eILmTEXpB8OT2eEpqqNpUv6++f+8UBlR1J6SdHUuHthS/9x4Hr5hKxqTJOA5qPaH2v/M5eq+ToKtnsHn+YdYuzSLAA1G9tM/KaDZgdBy7B9A9MIJlXeJomzeTjwKPJfTbSDj2kMv5szseSSzDtq8lLCcXnXUdUnrZHRVKZ1lBXCtIe3C8UwZ6IYRFCLFBCLFNCLFTCPFc7fI+Qoi1QojtQogfhBCNXssIIUKFEHOEEHuEELuFEGd3wlEFOG4S5RZ0553yh+FE+2ntxzmVSfzwtYeZf9pC5sL6E1Fs2/QNHSsyKBj8O8RJcps05cbFT1NYspyeCbeyYvr7+BvPbKh4Xt4WNgsHUyJ6I5qZf925fSXWpy8j3GulOqR+ZsMDB5ah03nx+sG+I9q4gQC9pDLYSLc9uwn99kcO/fVNNk2/mxzhhzU8ltIq7Sam9EoiGskYuXpJFsFewWqzm95/W0ivJ35mzFMLuP75Zfzh7XW8PjuduYsPsm2PlerqU+eo+X7dHqq9Bqb2PnGQW2RkJEajkcrKSq655hruv1/rErlhwwYCAgIoKSlhzZo16E/y5WrSm7iz5518d9V3rLt5HbOmzOKd8WenO21zWZLDMCUEUbH0CNJzrK3e0kOba8Fb4cJVUIDz0GHteXk5BS++iNfrxb3gO2oiEsmsjGDdtwfZ8qV276nmuF+P3z52rMlnxrWjcb67hWs8pVTp7fxf8sesrnRj84LFYGFGznJMaRvxyzlA/wF9EXgp93OQKZIYHbjr3H8YZ1lzmm4cwDgpZaUQwgisEkL8ArwNPCqlXC6EuAt4DHi6ke3fBOZLKa8VQpiA1nXN00pIbwtr9EBgQnuuebEtucuXEdGzBwdSV7BifRsOrM8k8bKj+/UQsvIlMv3b0W/YHS0u18vpc8ku/IXkuOv5fOzjLd6+Mb9s1rrPjUq+iY1rZ5PYZRThDheOr/+B8FSBoxxqKsBZgXBVoddXYPJzEGUAu9VIW8cP5K35kthh2iQVBYVrAdDXLCRaB4tsBg45dBRUF5Ce5ebBLzKoqHFDWhoARr3goQmd2XWghCK8TEs4cTDZ4aU5AEzoGEEvvOSV11Bgd7K/ws5GWxUypxi2H1vfTwqC9DqCDXpCTQZCzUZC/Y2EB5iJCjEze0s6Bvy5vGcibrcbw3FT8Pn5+TF06FBWrFjBwJSBJLVP4qmnnmLevHns3LkTgKqqxieIaYyfwY8+UX1OveJZJoQgaHQ7ij/dRc2eYvx6aG31ls5JeGv2YN+WQcbopwiZcQ3mbt1w7N5N1YqV7PzTy/iXH8F7y+9J7BXJvg2FSD89XrubT3fn8tHfi2gbauE3Izsw2mRjmTOYOR8t5+CY/YwK2sKmQjOrK3fSvvbGfbU3HLc04QywYKqqYe2mxQQCh5N3A3H0di0Bmk4EdzE6ZaCXWpvA0RkVjLX/JZAMrKhdvhBIpUGgr63ljwJ+VbsvJ3CK/nzK6ZAtbKM/Sm82027iJADihvaH9blEtz1W89u9ayndKzJYO+YVEg0tu4xfkreb/217CZNfZz4e/aeWFawpjkp+zl1Nbynp9OVtGKWHg6s7E1KUR4Cf9mvqdQu8bj1eaUQaLXj0bakK6UXerHWYQ9y0G1WCXPAQxTFdqNwwC7t5G36h4PbCV1vD2RZdQ7vgdhzICeCB2RsA+Pq+oSzbW8ji3YW8cm0fesWH8MKnaQBMG3Li8He/IBPV5U6uGZpIx/71+587nG4OZ1eQkV3OofxKjhRXU2hzUGp3UubycKiyhqqKamrqncsguuoL+OSTmQDopAGj8MPPGEiAfxCBgVpTwpcf/cg9v72TsDYBXHvttQwYMIDi4mJ69z5xisSLkaVrOPoQM6XfHaBmbymGGH+EQYfOEoguLhadeQjl874leOoVOHbvBsDw4ycAlMb2o3+y1lQzcGw7HMmBmPIrOFJSzabsMu4tyIex2g0crcqRyCj5AbdFOPi80s7ijh8y5vD1dDGtJ9MSxapeYxm37hcCS7X7WNvC2pNodHDlgNfO98dyxpp1M1YIoQc2A52Ad6SU64UQO4BpwHfAdUBjyR46AEXAR0KIPrX7eEhKeUL1QghxD3APQELCxZsc6OLV8hp9Q+u+TwOiiR02sG5Zye75uISe3gNblq5gX3keDy95ACGMzBz3GoEnyXffEssPLGev2cQTxSVs7H4ngaUH6J27FPzA3uNJTJPuQxcYjKFBk4515kxcVZuI7msDwB87/p+OIcirI2OMFhzKdofSb3MMf3v5ZRLadWFLprZuiJ+RXnEhDEwK57FJXev2ueJgMVFSR+9G8tkMu6YTP7yVxoG0ohMCvdlkILlDGMkdTp5WwunyUGC1k2+tYveBI1iqBCYZT2VlFVXVVVTXVGB3VVBeVoAs94IU+DtimfvqFqY+0IeY9sF06NCBDh2anxTsQhN6QfgtXbHNP0z1dityg9aLRhdgIvr+6yh6+yCO3XvwVlRiTk4m/p1/UfTND+xbn8+ujVVs26RNKBMa409ycjSjkrXPvsLhovOanfXea1TmPmRYAoEyjLenvMC/3/ucmsgN7C3KY+feXsx99y565rWlt8zGTxSRFdWByTrw87u489o0plmBXkrpAfoKIUKBeUKInsBdwFtCiGeA72m8pm4A+gO/q/1yeBN4gkaaeKSUM4GZACkpKRd/Jv+LjDzag+804vzGrfn88vEuIhzRhOqzObzwU4I7vU+ey0Ng1go2BfdgsF/9EYA5RVlIr5sg4WVfWSkDuwys9/o9K/6J12PjudHvkRLZ/jSP6kTv5m8DBCm3Lic5uhM//Opuwo6EUXDLfaRc13TK5ZJPZ4EQmO7/AsfXN2P2q6aoxp/vi5NJQpvwuzAtgvjuPWmfoM2NOqh9ODNvG8A9n24mdWc+0/seG0W6PaOEPfYaJkcEN/rlerRLpcly+jedTUY97WIDaRcbyMBeJ3ZFrSytIW3hEfRGQVzPIIIj/cBp4Pu30pj3+ham3N+LhO5np8fP+WROCCbqnt5IKfFWuZAOD7ogEzqTHulyIUwm3MXFGCIiMMXHE/fQ/cQBvXMqydhSiH+Qic4D639eQWYj8RYjPQL96FSUwzsiiLv7dGFCP20O4FWLFoIQ9O3eg/S52whL7onBYMQgJJ6AKLpMvZJFtjJi7GdvlPP51KJeN1LKMmAZMFlKuUdKOVFKOQD4HDjQyCbZQLaUcn3t8zlogV85y1raj/54C2ftwd8l0fUKJqLdYgYUz2XL+7/FXmGld8U+VoYOYOzGveTUaCNPXln9CwN2lJCyy0byzkquzDHidDrq9revPB9r6VoSIkczI/Hs5QHxer3szFtEQFBfkqM7sePmW+i0bjVF1jBS7noCr6fxvD0VixbhKSrCr38/LCljMfxhNS5HADnVIZTZAmmb+yvS3uuKs9LEiBvvqBe4x3eLIcTPyPdpuUgpmb8jn6QnfuLKD7R2/RtGNv4ldnTg2tF7J2eb0+7mqxc2sn1FNlsXHuGH13ay7OMDmAMMXPdECsERFlbPad5guIuVEAJ9oAlDhB86k/aFaUpKwl1YSE16OsZ29WvWEXGBDL6yA73GxDeaZCxK7ybVauMDjCSWHWJC716A9rezZs0adC4HoaFhCClp1y6Z/X/5Bbswk2YP4t29uQiHhz8OSjrnx30uNKfXTVRtTR4hhB8wAdgjhIiuXaYDngLebbitlDIfOCKESK5dNB5ofbesW4HTbaNP21lISJUXY7cQ7n8ghcmPf8z60CkMLviS5H93R4dkY+RA9lY7GLx6O9csSuU1ZyzTqrbzkiUbcfRSQh4LsveteB6km8f6nzh68Ux8fXg90l3EuMTJAITcp90Qc+n17Pnbbezp1YNdv5tMVc6eum2klBT++T6EwUvYwFBcB9LQRSehe3AJ3trLn5DeEwHBqFvvIi65fh4fvU5w7+gOLN5TyLAXl3DfrGPJwPqbLYwdGk9j3E7t8zAYz6wbaVPKi+zYK1yMu7Urd/xzGEOv6Uj+gXJ+fHsbuRllCJ3A5ThFwrpWKOTKqRiiotAFBhJ2y80t2lZfo/1edGIfvyn5Fzq9ntQ5X/Hpf/5FtVfSpV0c2Rl76Rk2kq772xMoQrkfMzcEmzHFBjAhIpiYIN+t0ccCS4UQ6cBGYKGU8kfgJiHEPmAPkAt8BCCEaCuE+Pm47X8HzK7dvi9w8lmoldPS3H70Xq+X/36czr6DpRzMtfHDO+k4hGTKlbUDY4Sg/wP/Y2vn31Msg9jpTaRrUH/+lDeHto5C1uhjuK46nf9MvJY7hk7lScNhAFwerYvgzzk7KSpZRs92MxjbJrmJUpyeWft+QAojv+uu9QVvN2okL992D/41NcjPNiG8ArEwk8y//75um6qv/oXTZiSyWyUhZbMwfjoa95MxuHav52BZR0zSQ0RnrWa3YtaHVJWVnvC+94/uyMMTOlPlcGM6btj7zIebHiVcbdNaMi2B56Yfuq52flpnjYeAEDP9JyYy8e4elFvtzH9vBzarnRHXdT7FXlofY2wsHRcvovOK5Vi6ND87qMfj4LENz/LJ9tv5s3yOzm3aU5SXy9oduzhYWAzAkFET6bS/LT1Ch1FuKcH/9kTuTIhkWFI0NUhubX92k7qdT83pdZMOnDCpqJTyTbSukw2X5wJTjnueBtrcyco5dJIa/YFDZXz+yQ68Hgk1HiIqvMzLrmQfLvp7dYRMiKVzUmjd+kajkX63/J2VOX/ktrfXcO3BVfw9621m+M/DOv2/9O90M0Kv/er0DIsAKyzau5np/cbx2tb3kMLEK4PObvczu9vJoaLlRIQOJtZPG7JRWF3ML8PGktjPyN12JzFTfs3++6bDmiO4nDZ0ldXkvfJvTGGC0P9spGbzfDz71mDOmYtY9DCZjuGEGf0w+/kz6Krr2PDt16yb+yXj76pfdiEED0/owsMTuiClZOprK9hprWTm93v48x2Nz7dbU6V98Z2rQB8eG0B0UjAbfzpEYs8IgiP96NA3ioTu4RQcthEa40/AOZyQ/ELSmUxgMrVom5L9C4n+QAAuXLEGMsfv5Z2NWg+mACHp1qUzrnl5hJvbcLAincGP34E5LJDcb/NYEmnET6dnZFjrSWLWkEqB4CPqkkHVBnprqZ2vvtpDVbkD/4PVhAF2A3VfCJZsO93i/QAPOVm2Rvf59Y48DLh5KuevAOQlz2BI8pB664zuNowOCxfy70Kw7ltCYfFSusXdQLvAszuz0l82foTw2ugaHEeNqwaTXvDqrkVAMpMHjCM+RsuuGNhvGFXr51Gy/We8//oId6Uk8f+ewhDZFsOku2DSXVR/24//fqnlrbFJDx/94T56jr2MTgOHcmjrRk7WR1oIwX/uTGHUK8v4eFcuj9b0wmQ58c+oulyr0RsM52ZCCiEE427vytxXtjDnpU1c/Uh/wtoEYDDpiety9iaJaY08FRVUb9yE9LiR1dW4rcXUbNC6ybrb6zEegrY/VpA+0U3f7t244sZb8Nrd5D6n3XeJvnwIxhB/bIuz8NicLA82MTo8qFUlMWtIBXofcbSp/GjTzcfvpuGXaceCpNQoiOoTzqN39wVg+14r3324k7BsbRq0wH1VjPzPSjrGhbDtUAk1Dg8CqCp3EB9YSai7klUj/sGICb874X31BiP3mYt53JvEkQ1/wWBqy3sj/3jWj+9I3lf46yTXeD7inZVpvM4TeEQyE/yyGRJzbFh/1FW/ourdeeTd+QY6r4uYcZ3xn1w/CZb/VQ/Qfc1y0jKr8Xg8lORms2K2ltemTadTNwckRARwW3QE1sM2Zj+7jhv+Mgj/4Po1zMBwrS338PZiug0/NxODR7QNZMafBvDt61v45uXNjL45mc4pp58ozhdUrV1LziOP4ikpqbdcmM2E3XorMX/5M6vuv5/w5Sv4w+8fxj9Sq5Do/AyIaW1w/ZBD4Kpq0ncsQm/QczBIkG8WTIpsHXnnm6ICvY9IX62NxJzz3T7adwjFk11NhUkw8YE+9OsSUa/tvldyJL1eGs3k99YQbfPil1dD9mEbRzJt6AON+AcYkQISEoK5a3B7+BZCvDVNvne3uM4EbfsPek8p0lPKfT+M4eqOVzKj12OYDGd+86rSUcrBykJGRMRji5pMX+vb3MT/WCfG896ASfXW9UvqgnNSF1Y7HiKwIotbX7660X2Of24mPZ/rTk70lfR68C3S5v/IwS0bGXXLnY2u39AzDwxk34Z8Vnyxjx/eTqP32HZ0GRyDvrbW13tcPBt/OsTBNCuLP9nF2Fu6npMJwsNjA5jy297Mf3c7Cz7Q+olfqsFeSknWnXcBkPDxR+iDg9H5+6OPjEQXEFD3N5B4/fVULVtO+rPPMuSdY2ke4oZ1xjOoAzs+XkXZwXKWmXayMbErQoYxMqBlTUUXGxXofYQr0ADFDqwLc7GSix/Q4/qO9E8+sQnFWu3ksmU7yOviTx+nji9+N5j9n23i0dEdmdLx2A2nsqpy0udpI1plcNO10sf3zMds3wJAG6PksN3OC+lf8eneeVyROIZ7B72EQX/6bdU/7n4Xp4Rpna9nfJe7+O/aDC63/8Q1wXYCjNNPWP9Q8gxIh8qgBHZ+t4LetzSS+KyykBhLFTFjR4HJzMBpMxg4bUazy2Q06+kxMg6Tn4G18w6w5H+7Kcq0Meom7Qa0Xq/jxqcH8flzG9izNp+cfWXc9OxgjKaz0wsna2cxm+dnEpUYxJFdJVTVNhWt//4gHftf/HOYngvSdSxnkCkpCWObNo2ulzBuHGt79SJwxUqk2404Lp2E3qDH0z+IZdla//r8tonE2IpJW5FD9MSJmFp4b+Bi0XobnZR6jCOjeTPEjjM5EPPACK5+aiDjRzc+OcXU5bvIswgGewx8OaYbYRYjS+4aWi/IFxYfwfVmP0ZlfMGaTjfQNeXaRve1K+MXisq+0cogJO9O/JiVN67jqb43U+Vx8+6+hXyx7cw6Wv1yeD6hesGYTrcBcOfgNzCYYrCXLSEz68N6625a/As529vSrk8uSSF7WbkygOX//AiPvbL+Tte8CToDJJ5Zjr3OKTHc9vehJPWKYPfaPLJ2Fte9Fhzhxx0vDiM6KYiK4hr+9+c1VJY2fWXUEnvX55O7v4xti45QkntsoHl5ob3e80uJzmQicdan6AICOHzTzTj2729yXePwYRhcLvJXrT7htcWLFwMw4aobyDH5kWTNZ9OmTXz//ffnrOznmgr0PsLp8eIUcO8D/bn7131oF9/4xAhLs0s4bJakeAx8N6EnoebGa9qHNswmylnMrinvMezWmZgMjddkblh9LIfNP4c8QsfIFMzGAG7o8yRLbtpCpEHHZ/u+o6gy67SOq6jyCGnlxYyK6YK+NmWuTmdkcMochDCSkfECNpuWHazKVsyGuYLAqELG33oFE5+8nqTILHZkJrL67W+wHdhL6jMfUPa/h2DrLBjy27rJz8+E0AkGXdkBS6CRH97exg9vpZG5Qwv4Fn8j1z6eQki0HzWVLj55cg07Vuac8Xt2G972hGWWQCNJvSMJj7108wb6p6SQ+On/kG4XR+67H09F4/MtJE6dikenI/fRR8larQV7t9vN66+/TlVVFQkJCeypbb+/L0VL8Obnd2ZZVy8kFeh9hMtdO+T+JD0DHG4P92w7hHBL/tkvqemdSUmbXV+yI6Q73Qfd2PRqx41E/WTcP5nUpX77tl5n4MHed5DtcHLlvCtYceCz5h3Mcb7d9Q5eBFcn315vucXSll49/wVItmy9GZe7ii1LFyM9Jkbd0IOAoDCMoRFc8Y9f0b3dYbYfjOPTV3LIKOzA7DXTkZ0nw9lKtAZEJQRxy3ND6DkqjtwD5fz073R+/k86c1/dzH8fXUl57dy1AGu/yWDOS5uY/ew63K7TG9R0cGtRvefBkRbufGk4V/y2N7pW3DvkbLB0707866/jysnB9uOPALXpFI5d6UR16kTIG6+j83oou/c+ds+ezZYtW7DZtB5oLpeLVGs5nf3NGKxafnyzufV2V720fyN8yMwV2gQM+ibaZu0eL8NX7aLCX8/tISH0iqzfJ9jp8bApJ48al5vSyhISKw5S3PHyk75n3oZHeDmumnmdRtG/XeMzT83o9Uf+N/5VAN5Je7ulh0Vq1jJiTXr6x5+4/6ioCbSL/xUeTzWbNl7P3tXgH24lqXv9TI1D751Gu6BD9ZbZJn4E5rPbL9pg1DP65mTueGEYPUa0peiIVpvs2DeKIVd14JbnhhDbMQRnjYeCQzbKCqopzas+rfcqOGwjIMTE9D/0465XR3D9nwde8gH+eH59tFq4p6wMgNxHH2PvgBSyH/4DXoeWrqPdpEl0mDMHR1AQ9rfeZsCAAXTqpA0crJKwtqySSZEhdO6sDTzLzj47c/9eCOpmrI/wSkmvuJAmR8auKLGRXZum4C/96rfdHym3MWndTkpMfvjvzOJX+mKeAfR+J+9SVly6BpMFkvo/d9L1+sZPJtTwJLn2KjKsW+gU2bx0R+mH5rC3qorr2nbG466hMH8PbRLqb9uly9OUlm0gZ2cgDlskQ26oOeEzMIdHED/+Mjqb7Ozf4cB6pOKcDiayBBgZfXMyozlxZPDVj/YnY1MhlaUO1n57gK//uZER13em99hjeVtsVjt5GWWU5FXRpmMoST0jTshhFBkfSHlRNfHJl3af+aZ4KrV7MrqAQDyVldh++glTYiIV8+djGzmS0BnXABDSoQP2Af2JXLQYe0kJERERZGRkQL9BuKsh7MAeZm/SppC0Wk+cfa21UIHeR0hgSIfwJl+fFBVa9/jVbVn8beCxtul3d+ynxOTHTVSzzuPBL2cBAHHJ4076nu0S7qSw8FWOrP8d7cd8ftJ1b+96Nf9M/5rrf76dH6d/Q9uQU6dH+GqT1re9s2sHy368FV3oNgqPPEbv4fUHNPXt8xlbZ2m9JNoPGHLCfqzZlaz99mDd86FXd8Rwlnq/tJQQoi6zYvu+kaz5JoOVX+5n16o8nHY3eqOOsoLja/lZxHYKoceItnjcEo/bS/KQNpQX2bEEnJ9p/lojT6mWykIfEY63tp0+9PrrKXzlFdwlxfXWjZs4kV2HDrPgxRcRtSnSd/uFEOGsonD5irqEsJWVlWRlZbXKNOrqWs9HeKU8ZZ6bW83a5BTRfvUDxJaqGkIc1bw+ZijLLhtKR5cWaMTWH0+6v9Ce9xPs9KO4Ysspy3dzv2d4IeXXuKRg5aE5p1wfYG1JCbE1eiKNHnSh2wDIz/up7nW3y86q+b/j2/cfx+MMBGDm8wsoqayfryaodvBSYLiZ/pMS6DW28URk51totD+T7+lJn3HtcNhdxLQPJjTGn6FXd2Taw325563RjL2tKyV5VSz6eDdLZ+1hxRf7+O8jK8nZW0r3Rm7IKhpR2w2y9NNZZN39G4C6XjimhPpXtO2nTmXzwBTKgoIoLS3F5O/PsrJKUnTawEGz2YzRqP3NVFY26L3VSqgavY/wylNnrrTX3rAdEH2sR85n2/ewXWehr7sCIQRmg4Gr7nqHwpfWkLT1RaqG3ERATFKT+ww1deaIYRue6iL0/iefBHtS1/v46+b/surIIm7o+5eTrrt532oKLZXc4j+FAI8Xu34uALrQXSye82sGjv07m1Y9jjdwDdYd7wNQHJFJRHEi7z7/C9f9biid27Znww8H2fjTYQCGz+hMpwEXV2IqnV7HiOs7M+L6xhOQdR/els4DY6gsqaGqTGtb3rexgPDYAPqMa30TYJwvxvh4Qq65BtvPP2NO7kLAiBGUf/89ll69CBw5ot66aevW1T02GAx4u/aiwuNlckwwe4HOnTszbdo0jhw5Qvv2Z29uhfNJ1eh9hQTdKSL9T7W1kd7hWs3+jXnf82hhNcGOat4cfGyOUJ3BiGPIwwDkLP7PSfcZGNwdqRNUZKeesogmvYVhEe1YVVLEz7tn4va4cXocja771lfPILxw04hfM+SyFwgSN5Kc8DFuWwyEL2PthjHIoDW4SzrWbfPU3+4g5koPIaVtmPvqJpweJ/ZKbRDNxLt70KHv2c2/c74YTXrC2gQQ3zWc+K7hjLutG30nJJzW3AOXCiEEbV94nuStWwi96iqqVq0iYNgwEj/+CJ1//e6nvQZqk+ZE1NRw7bXXstRmx+Bx49yi5ccxm82YTCY6duyIrpmT0V9sWmeplRN4pTzl5FJRUjvdL23V+rSb49rh1en4S0IUHSPq39SLG307duFHTMYXuKobT3pWU7qLg8VfY3BL/MJ7Naucvx/4NAYBj294m36z+jF0dgorDsyqfyxeL/siiokttrDuvQ/R6Y0MGvs88Z1GMunq1XSMewMcsVQX+bMrLYia0tepTP4KnV7H+HEDcekcSJMHo86I3ebEP8RE55QY1SvlEiSEwPr++5jat6fdv99BFxBQ73UpJdXz5xNcXk6xxcLnX3zB4Yg2xJcWUpitzTx2tNdNa6Z+832E5NRNN/PH9SC00sXMShv9vk7l1VJt2HxpxYntjjqDCWvKo4R4y8hfNeuE1x22DFZvvRK3cNMv7s+YI/ucsE5jOkcP5fvpc/lDtyu4Nr4nTgkPrHqJtOxjUxgs3fYTlf4eOuQGkLtv97HMnGh/uEnJ0xh/5SLSlrVHZtReEdRoc4v+uGQZRq+ZoVO6kLWzhANpRSQPbnwovHJp8O8/AOehQxy+7Tas77+PbcECpLP2d3/WbHIf+xP9N28huLyckA5dqLL4c0+/Xjz22GM89dRTdO3a9RTvcPFTgd5HSClP2XQT4Wdiyaju3P3tF0SXFOOtDaCpew82un7cpN9TRQBsP/Hm6YbNWkqE7hF3Etzj7haVNTakC3cNepFnx3/OtQnaVAW3LX6cz9P+gdfr4Zv0L9B7BP2MXfF6PFiPZJ6wj9lfvYZ/iZeIq4eTeMsUrrpRy6yZvaaaGksFfbt3ZfP8wwQEm0iZktSi8im+pe2L/6TNX5/FYy2m6LXXyfn9Q+wfP56cRx6l8OWXCRg9itGLF/HgI4/gHjkWHTAtIRbQ2ux9gW8chaLdjG3GepYjmdyS+h3dQ+OYdvs1/PuV13DZq4DrT1hXZzBRFDmIWOtqPC4HeuOxvucdQ69md+n/0FnObPLpZ8Z8iP/K3/D54fW8sO1LXkn/AheCHs44Kg5rA1Sqy8vrbZNXlEXB9yvRI5gx7X6C/EMBqHJWE1ys1d5nPaXdYOs/KaHRfPHKpUMYDITdeCOhM2bgKijEefAApbM/o3rrFvz69SP2b39DGI2Y4uNJ3biHgSEBRJp863dG1eh9QHOnEQRIm6vdNO069TIC/SwEVVcQ5nE3ub6+wyjMOCnetaLe8qiOdwBQaV17usWu88iIf/PN1M+ZGtsVl9SOIXbPsdQA8d161Fv/45l/Re/V1nvlkVuwVZUBUGYvwyM8YPYw8oYu9Lssgb6Xtb4+z8q5oQXzOAJHjaLde+/SeckSEv/3CcYYbVxDlt3BzsqaVp97vjEq0PuAlkwM7li6hNywtvRL0QYslel0RJiaHiXqlzhA2y5vd73lhiAtgHocJ86x2hI7dv6eZct7oatYzVPDX2JisIsAnSS20EhwlPYH+M3zT9fbpveoCTgStX7zwSXgqNH6/ZtNJj7r/xz2G9PpPTaeYTM64RfYOtPKKuffgmKt08FkFeiVi9HRW5XiFI03O9dvJykvA9f4yQghcFdV4TAaCQxp+hfbnp0OgCG0/uAcIXToPeD2nF6uFtCuRIqKFiClm4wDL7Nu/WVMCXHx1yALeo+O9v1SCIqI4siu7TiqqyjLzwPgipE388Q/Z2O3eHEkBhIVoZVNL/RUmcop9ZSc7G0VpVHzi7QkZh38W2/ysqb4VkPUJepo082pulUf/Oxr2iNIuftmAJyVlSAEBeVlx/bl9VK47H3caV8SXLGfdtKGTQQTPWDaCfvzcxkpFtl43XZ0hmMpXKWU2s3hU/Q5FkIQHNyX8vJNGI0RdOjwMBHhI3FWGLHYfqTf5KlEJ7Vn4cx/8c6vb0Kn0zHjz3/DEhXOx/95Br8aHeH9+iCl5Jv93zB792z0Qs9dPe9q5ienKJoyl5u15ZX8tt3FNaDubFGB3gd4m9l0Y0zbTE5Me3omabNFWaK0kaxZBgMHVq6k48iR5Hz1J+L3vI8TIyWmOIKcNor7/JZg44lNILH6buw3prNu1Uh69P8QizmWLRse4OC6w5TsiaTP5UNxu12k/7wRSwjE9+rI5b9+Hb2+dnh66TrKyzcBEBExivg47QvIzw/G3K715OkxejzWI5mU5edxZEc6X734NE6vC71X4OwZxXXTH2RPyR6eW/sceqHnjTFv0DG04wllVZSTWVJSgUf6ZrMNqEDvEySnvhkrpaRNURY5wy6rW6bT6fjNlVcy6+uvmbVwIUM2b6arbq/24qP7aBMQhi3/IElt6k/OYTswh4P7XqDYXI7wSuyUsmnTsblZwzqZqMiMYePX62uX6NEbjOxdfBjr4Ru57ol3kFKStuJ9ZAAIHXRN/luj5dYbjIz71b0AlObl8Pd/3I5HJ8kcYODDG18n0BJMtNBGv17R4QrGJoxt0WenKADzreVEmQz0C/bNSVtUG70PsNrs/GR6klFLZ7Dpq5fIObwP6fXWW0cIgd3sj8NeP+VA3IAB3HvnnSTUONhUWkhiyQoqdMEY/UNBCIJjO9Z9gUhXDQeWXs/mQ3/CpiujgxjIqKGr6d/vM/Qcy3MzYMAX3PPWd/S8XLvhO/yO8fzmjXn0mdab4gNO3v3Nb5h5/91s+ayAbe93w5bRAb3+1H9gYbFx/O2N70j+9bUc0hUwcc5E5u6fy56SPQAcqThyJh+jcolyeL0sKbYxMSL4lGNRWitVo/cBERZJnK52UNGuF2DXC9jwJ9+USFVIZ4jqRkSfy/EKHboGXwAAocnJXP+nx1j+yv3gB15LG4QQSI8boT/2K3Jo6fUcNuwk2hVF8ogvMQUlAVC4fwlOVzFep5m+fT4lJkEbJTvpV68x4TY3+tp9TLjlBeI7f8eO5T/iqK4mf5fWP95Z1PymlkBTIL/t+1smJ03mxQ0v8uyaZ7mvj5a2uF2QSvKltNya0koqPV6f7FZ5lAr0PsCir61xT/gbGSFDKdu9FE/BLgLLM0gqXIq/9XvuKfmIJ6oNWP0bn/cyIDKSAeOHs7toDV5dLjvnd8Zh9BLljadd7+dwF+3gkH4HbdwJdJ+8FCEEXo+LFfPvxG1Zi8fuz4CU2UTH15/dSa+v/yvWddB0ug6azuFtW/hm1zN0TBnC1Icfb/ExdwjtwNvj3+Z3i3/Hu9veBcDlcbV4P4oy31qOn07HyLCzO+PYxUQFep9Q20av09G51yDoNajuFZfbw4705djm/x7w0GX1L2TuvJ7EHscm6HCV2Mhc9zJZ4nNkrAWjywt4iXCGU2DJoWCXdmPU32mg65hvEEKQsX0u+/e/hCnUire0F6MnvY9/4MnTFNe9n6OGrfN/ALTBUAbj6U2gYdab+efIf3L7L7cT6RfJLd1vOa39KJcuKSULim2MDQ/Cz4eT3qlA7wtkbXOMOPEX1WjQ06fvaGSaiXlDa7h6rST/tt+x+7qbmfjHB7Dv2MvGgtvxWLTBIoPbf0lg0oC6LjyOvPVkbn8GIfQkDn0bvV8Euza/T07JS+gseoK4jYHXPNusUblHLXz/HQ5u2QjAobTNpEy9+hRbNC3CL4Kfrvnp1CsqSiPSK+3kOVw80d53m21ABXrfcDTQNzJgqrq6mOe/v4kDeheMNHD5va+ydWYJnsxoPvjtPEZTQWzyPWSnaBN4Z+z7kO4xXTD5a5OTmGMH0yX2WK75XVvfI7f0ZVy2EEaM/Y7g8Ja3i2dsXEdc1x70GjeR2M6nnlJQUc6V+UXl6IAJEcGnXLc1891rlUtJXQ6EE0/nE99M43tHHqFeyVc3r2JBqhW3MQy3YzPB3vUERCVjdscyqPt8Qu2jKDamkrHmtUbfZl/6p+QWv4yjNJThY5of5O2VFWz55QfyM/ax9pvPcdXYEULQY/R4wtteHNP6KZemVGs5g0ICiPCxJGYN+fbRXSqaCPSuGhub3GX09up48bJ32Z9TgH+2AUfFh5iEhwFxv6bUWUjXP16J0WJiwBUfsTS1LxVOLa9NdvY6omN6YzL6s2v9bHLK/oarKpDhY+YREtH8mvyyT95n14ol9ZaFtok9s2NWlDOUaXewq6qGZzv6/ty7pwz0QggLsAIw164/R0r5rBCiD/AuEAgcBm6RUjY6FZEQQg9sAnKklFPPUtmVo+ra6Os33cxf808qdDru6XUf7RJG8NZ/vsBl0yYRSQjsh58hEONlERgtJrxeL7tXbKPSJaiUmSxe8jvgZ7bvCCYm9NcUlbyJ9JgYPOQLQqNalhHSaa+fD+fe/3yCX7BvXyorF78FVt9NYtZQc2r0DmCclLJSCGEEVgkhfgHeBh6VUi4XQtwFPAY83cQ+HgJ2A+qv+5yonwOhtLqYP/40Gof0EKHXMbD3bdiddpy7/dCFDIXydVj02pRqiaNT8Ljc/DDza9KK9hIb25VOnTcA2oxPJpON0uo3QBpJSfmciNhuLSpZcXYWGZvW1z03GE0Ehp9ZDntFORvmW8vp4m+hvQ8mMWvolIFeahmzjs41Z6z9L4FktJo+wEIglUYCvRAiHrgCeB7445kXWTlBg143W7a/wa0R9rqXV63sR5XTn7gOkwkNv5Iu2wdh1luo9lSSvnAjyzesokRW0Du8C1NueZhV63+Nn6Uvgwf/iSVLe6PXuwnQTyAytndj794km7WQnSuWgJTc9tJbpC/6hcTe/c7aYSvK6SpzuVlXXskDPprErKFmtdHXNr1sBjoB70gp1wshdgDTgO+A64CmGm3/D/gTcNLRCEKIe4B7ABIS1GQRLXJcr5uaws1Q9TkAOo8RR/R1lFXsI0psIrj3XCqWdGeVxU6iJ4pdpiMUrrcRogtg+tDJ9JkwCJ1Ox4Tx39TtOu9IX+KTNtGx+50tKlJFsZUPH74Xj0sbxOR2Oplw9wNn5XAV5UwtLrb5dBKzhpoV6KWUHqCvECIUmCeE6AncBbwlhHgG+B5wNtxOCDEVKJRSbhZCjDnFe8wEZgKkpKTIk62rNHBcjX719utACOIcbUmetByh0yGl5OdP/4E+5nPSDFqqhEP6QgKEhUm9xzBw6ggMxvq/Ch63h1VfLuBQVjLu8m6MHzegWUVxVFexYvZHmP0D6oI8wO5Vy2jbpfVPsqz4hvlWG9EmA319NIlZQy3qdSOlLBNCLAMmSylfBSYCCCG6oDXPNDQcmCaEmAJYgGAhxCwp5a1nVmylnooCjrS1kJf9NARpo0y7Xr6y7mUhBD0HX8nPS7XkX9dNuR4/nZ6EXh0wmI+NSvW4PUiv5OCG3axYuYJsRxFmaaKvswdSyuZNVZj6E+mL5tc9N5jMuJ0OopM6nGQrRTl/HF4vS0psXB0d5rNJzBpqTq+bKMBVG+T9gAnAS0KIaClloRBCBzyF1gOnHinlk8CTtfsZg3bzVgX5s6zGW86+TtrUeu08nek4/H8nrFNmLaEgvzMAXfslozfqT1jngxffIc+tzc5kxMCw9v2J3OckujSSvPU7aTuk50nLIaVk96plxHbpSp8JlxMSFUN8957UVFViCQg808NUlLNidWklVR4vkyIvnb4hzRkwFQssFUKkAxuBhVLKH4GbhBD7gD1ALvARgBCirRDi53NVYOVEMqoTAN27vUKXy+aj9z/xBpN3e2bd43/98w2yth2o9/qRbQfIc5fgJ01MHzSJRx97lIl3TCNiqHa/xOtsegLxozI2rqU4O4seo8bRY/R44rtrXwwqyCsXk1RrOf56305i1lBzet2kAyd0lZBSvgm82cjyXGBKI8uXActOp5DKycmT5LoB8HrdhOVFc1sbE1Udu/Dt6p/57vvv6LS5PTuO7GVQjwEs3bEaI3oefOhBAsKP1XQ8did6wHiKtkxHdTW/vPMGMR0603PsZSddV1EulOOTmFl8OIlZQ2pkrE+ozV7ZxAVa6a6tGBzBmLoY6Dh+IDu3bWdvVRbFWdsAWLpjNQCThk2oF+QB3MKNCT3SeWIee4Ds3Tv45Z03sBUVADDs+pvRG04vG6WinGvbKrQkZr6ce74xKtD7ANnEyFgAr9eLbX4Wwmym7ZDhANzw8B2sn7eMnQf2MOO26zmy6zD+oYF0GlR/MJSUErnTDgRiLy6rW+6ormb3yqVk7dzG4W1bcdXYie/eE4PRRFxyj3N1mIpyxlKtWhKz8eGXTvs8qEDvEyRaoG+sRp+/ej5GazTeMYWYAsIA0Bn1DL1+PEMZD0BYfON55PfNWkKAPRBrQAG9JlxVt3zH0oUs+9/7AEQldaDHqHEMuOKqRvehKBeT+ZdIErOGLq2j9VVNtNHbS/NxLK2BYCeJE6a3aJfb3/qesNwwbLoSevxpGvrj+tlHtNNu0A699maGXXfzmZVdUc6TTLuD3VU1/PUSSGLW0KVzN8KH2e1ajxpxXD56d2U1BTPXoa8JJeyqLugMzf9OX/vuLMJyw3B47HR68jKMZlO9100WbTrC8DiVYlhpPeqSmEVdWu3zoAK9T3C7qwCwWLSairummiPvpKIrD0Z3RSmh3bu3aH+VRcUAmPV+5G3YVe+1ktwcDm7ZAEBgaPiZFl1Rzpv51nKSAywk+fl+ErOGVKD3AVJqfdyNRi0rZO78+RhLI9FPqSR++IwW7++ypx8i8O6OANi25Bx7H6+X2X/+A+vnfQWA260m41Zah9LaJGaXSm6bhlSg9wFeqQVcnU5rnvFuM+IMzyVuxLTT3mdop7Y4PHZ08tivyOFtW3DaqzH5+XPlH58kSWWiVFqJo0nMJvn4lIFNUTdjfYCWcw60JKPgCSnDUByFx2lHb/I77f16pRshtXb/zPQ0vn3lH4REx3Dd088TEt3mzAuuKOfJfGs5MZdQErOGVI3eBxxtutHmhYGAoW3RuwIo2bbljPbrFV5klbbvXSsWozcYuPXFN1WQV1oVh9fL0pIKJkaGXDJJzBpSgd4HSK/WdCOEdoEW1F5LXlZjLTrtfXo9Xgw6E4Ya7cujrLCAqKQOKm+N0uocS2J2abbPgwr0PuFo083RNvrSNK0mb4lufCBUc9hzSzELP2qiHUgpqSorwS/o0mzfVFq3+bVJzEaEXrqVFBXofYC3runGgLumCvdaHc6wAiL6DW3xvqRH4jhYRtmsfQCUVOexY+lCygvy6ZQy+KyWW1HONa+ULLBeeknMGlI3Y32AlC5AIISe7G+/w2Bvh2W6EZ2uZb/YnioX1g+248rT+uUfsKWx9dBC5M5fEDodPUaPPwelV5RzZ1uFnXznpZfErCEV6H2AlB6EMFC6exv6tDicHbKJ73tTi/bhOFxO0XvpAASNa4cpIZg2HYcQvaE3pbnZxHTojGjhF4eiXGip1nL0AiZcot0qj1KB3gdIr0sL9Kk70ZtCibt5Yov3UbO3FCQEjY4nZGJS3fJuw0efxZIqyvl1NIlZuPHSDnWqiuYDHI4CTCWxmPLbQb9qzIERLdpeeiUVS48AYEq6tGs+iu/ItDvYU1VzyY6GPZ4K9D6gOGsN8Vv/iMdUQZsJLWtH9zo9VKzIBsDSNRxLsspfo/iGVGs5gAr0qKYbnxBTchN6VyCBd4dgDgpr1jaeKhfF/9uFM1PL6GeMCyT8pmSE7tIcUKL4nvlWG10DLCRegknMGlKB3geEZA/Hg5PQTr2btb4rv4qC/9P62geNT8CcGIy5YyhCr4K84htKXW7Wl1fyYELMhS7KRUEFeh/gKXMC4HV40Jn1J11XSknpvAxAa6oJuSzxnJdPUc63RUeTmEWqe06g2uh9QsjlSdoDT+MTeB/Pvq0IZ6YNU2IwEbe3LE+9orQWdUnMgi7NJGYNqUDvC5o54k96JeULtNmoIu/ortrjFZ9U49GSmE26hJOYNaQCvS+QtT9P8UttSz2Mp6SG4AkJ6PyN575cinIBrC6rpPoST2LWkAr0PqE20p8kztfsK6ViudaNMmh8wnkok6JcGKnWcgL0OkaEXbpJzBpSN2N9QV2NvvGXq7YUUPrVPhDQ5tEUhLqcVXyUV0pSreWMDQ/CrFJ21FGfhC84GuibiPRl3x4AIObh/hgiTn/GKUW52KVVVFPgdKtmmwZUoPcJTTfdeKtdSKcHU1IwxpiA81ssRTnPUq02lcSsESrQ+wB5NM43CPTeahd5L24EIGBw7HkulaKcf6nWcgaHBBJ2iScxa0gFel/QSK8bV34VuX9bh3R6CLk8Cf++pz/blKK0BseSmKnafEOn/NoTQliAFYC5dv05UspnhRB9gHeBQOAwcIuU0tZg23bA/4A2gBeYKaV886wegXKsSo8W4G2Ls7BvtwIQMDSWoNHtLlTJFOW8mV+bxEy1z5+oOdc3DmCclLJSCGEEVgkhfgHeBh6VUi4XQtwFPAY83WBbN/CIlHKLECII2CyEWCil3HU2D+KSVxvn7TuLKfl8T93imEcGYIxSIwOVS8N8azndVBKzRp0y0EspJVBZ+9RY+18CyWg1fYCFQCoNAr2UMg/Iq31cIYTYDcQBKtCfRcYYLZgfDfJR9/XGFBeIMJ48742i+IoSl5v1ZVX8PlElMWtMs+5YCCH0wGagE/COlHK9EGIHMA34DrgOOGn7gBAiCegHrG/i9XuAewASEtSAnpbw6xlJ7F8GU7W5AEO4BXOSunRVLi2Lim14Uc02TWnWzVgppUdK2ReIBwYJIXoCdwEPCCE2A0GAs6nthRCBwDfAww3b8Y97j5lSyhQpZUpUlLpx2FL6IBPBY9rh31t9dsqlJ9VaThuTkT5BapxIY1rU60ZKWQYsAyZLKfdIKSdKKQcAnwMHGtumtl3/G2C2lHLumRVXURSlvqNJzCZGBqskZk04ZaAXQkQJIUJrH/sBE4A9Qojo2mU64Cm0HjgNtxXAf4HdUsrXz2K5FUVRAFhVm8RMTRnYtObU6GOBpUKIdGAjsFBK+SNwkxBiH7AHyAU+AhBCtBVC/Fy77XDgNmCcECKt9v+Us34UiqJcso4mMRuukpg1qTm9btLRbqI2XP4mcEKfeCllLjCl9vEqTppTUVEU5fSpJGbNoz4ZRVFarTRbNYVOt2q2OQUV6BVFabVSi7UkZuNVErOTUoFeUZRWa761nCEqidkpqUCvKEqrdNjuYG9VjWq2aQYV6BVFaZXmF2lJzCaqbJWnpAK9oiitkkpi1nwq0CuK0uoUO91sKK9SzTbNpAK9oiitzuISlcSsJVSgVxSl1Um1lhNrVknMmksFekVRWpW6JGYRwQiVxKxZVKBXFKVVWVlaoZKYtZAK9IqitCqpVhuBeh3DVBKzZlOBXlGUVsMrJQuKyxkbHqySmLWA+qQURWk1jiUxU4OkWkIFekVRWo351nKVxOw0qECvKEqrMd9qY2hIIKEqiVmLqECvKEqrcKjawb7qGiZHqd42LaUCvaIorcJ8a20SM9Vs02Iq0CuK0iqkWsvpHmAhQSUxazEV6BVFuegdTWKmctucHhXoFUW56C0q1pKYqfb506MCvaIoF71UazltzUZ6B6okZqdDBXpFUS5q9qNJzCJDVBKz06QCvaIoF7WVpRXYvV41GvYMqECvKMpFLdVaTqBex9BQlcTsdKlAryjKRUtLYmZjXIRKYnYm1CenKMpFa6utmiKnW+WeP0Mq0CuKctGaby3HIGB8eNCFLkqrpgK9oigXrfnWcoaGBhKikpidERXoFUW5KB2sdrC/2qFGw54FKtArinJROprETAX6M3fKQC+EsAghNgghtgkhdgohnqtd3kcIsVYIsV0I8YMQotFOrkKIyUKIvUKIDCHEE2f7ABRF8U0LrOX0CLTQzmK60EVp9ZpTo3cA46SUfYC+wGQhxBDgA+AJKWUvYB7wWMMNhRB64B3gcqA7cJMQovtZKruiKD5KJTE7u055h0NKKYHK2qfG2v8SSAZW1C5fCKQCTzfYfBCQIaU8CCCE+AKYDuw645I34Yu8EhYXV5yr3SuKch5Uez1aEjMV6M+KZt3Krq2ZbwY6Ae9IKdcLIXYA04DvgOuAdo1sGgccOe55NjC4ife4B7gHICEhobnlr+fhxBi2V1af1raKolxcpkaZ6KWSmJ0VzQr0UkoP0FcIEQrME0L0BO4C3hJCPAN8Dzgb2bSxDESyifeYCcwESElJaXSdU3koKeZ0NlMURfFpLep1I6UsA5YBk6WUe6SUE6WUA4DPgQONbJJN/Zp+PJB7ekVVFEVRTkdzet1E1dbkEUL4AROAPUKI6NplOuAp4N1GNt8IdBZCtBdCmIAb0Wr/iqIoynnSnBp9LLBUCJGOFrgXSil/ROtBsw/Yg1ZL/whACNFWCPEzgJTSDTyIdqN2N/CVlHLn2T8MRVEUpSlC61RzcUlJSZGbNm260MVQFEVpNYQQm6WUKY29pkbGKoqi+DgV6BVFUXycCvSKoig+TgV6RVEUH3dR3owVQhQBmae5eSRgPYvFaQ3UMfu+S+14QR1zSyVKKaMae+GiDPRnQgixqak7z75KHbPvu9SOF9Qxn02q6UZRFMXHqUCvKIri43wx0M+80AW4ANQx+75L7XhBHfNZ43Nt9IqiKEp9vlijVxRFUY6jAr2iKIqPa5WBXgjRVwixTgiRJoTYJIQYdNxrT9ZORL5XCDGpie3DhRALhRD7a3+Gnb/St5wQ4svaY00TQhwWQqTVLjcJIT6qnaB9mxBiTBPb/1UIkXPcPqacx+KflrNwzK3qHMNJj9kohPik9ph3CyGebGJ7XzrPzT1mXzrPtxy3PE0I4RVC9G1k+5afZyllq/sPLAAur308BVhW+7g7sA0wA+3RJkPRN7L9y2gTmwM8Abx0oY+pBcf+GvBM7eMHgI9qH0ejTfeoa2SbvwKPXuiyn+djbrXnuJFjvhn4ovaxP3AYSPLx89zcY/aZ89xgeS/gYBPbtPg8t8oaPdp0hMG1j0M4NmvVdLRfDoeU8hCQgTZBeUPTgU9qH38CXHXuinr2CCEEcD3ajF6gfbEtBpBSFgJlgE8NMDmDY26V5xgaPWYJBAghDIAf2rSdtgtUvHPiDI7Zl87z8W5qYvlpaa2B/mHgFSHEEeBV4OhlXWOTkcc1sn2MlDIPoPZn9Lkr6lk1EiiQUu6vfb4NmC6EMAgh2gMDaHySdoAHhRDpQogPW8Pl7XFO95hb6zmGE495DlAF5AFZwKtSypImtvWV89zcY/al83y8Gzh5oG/Reb5oA70QYpEQYkcj/6cD9wN/kFK2A/4A/PfoZo3sqlX0Hz3F8R7V8Fv+Q7Qvs03A/wFrAHcju/8P0BHoi/aH89o5OIQWO8fHfFE6zWMeBHiAtmhNko8IITo0sntfOs/NPeaL0mke89FtBwPVUsodTey+5ef5QrdRnWa7VjnHxgAIwFb7+EngyePWSwWGNrL9XiC29nEssPdCH1MzjtkAFADxJ1lnDdD9FPtJAnZc6OM518fcGs9xU8cMvAPcdtzzD4Hrffk8N/eYfek8H/faG8Cfm7mfZp3ni7ZGfwq5wOjax+OAo5c+3wM3CiHMtZf1nYENjWz/PXBH7eM7gO/OYVnPlgnAHill9tEFQgh/IURA7ePLALeUclfDDYUQscc9vRpoqqZwsTntY6Z1nmNo5JjRmi7GCU0AMARtruZ6fOk808xjxrfOM0IIHXAd8EVTG57Web7Q32yn+W04Aq23xTZgPTDguNf+gtbbZi+1PXNql38ApNQ+jkC7obe/9mf4hT6mZhzzx8B9jXyb70WbeH0RWprSxo73U2A7kI72hxF7oY/nPBxzqzvHJznmQOBrYCewC3jsEjjPzT1mnznPtcvHAOsaWX5G51mlQFAURfFxrbXpRlEURWkmFegVRVF8nAr0iqIoPk4FekVRFB+nAr2iKIqPU4FeURTFx6lAryiK4uP+HzvgjqwpoASDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#find all tracts and precincts centered in this clipPoly, flag for cutting\n",
    "# and, ID all nearby tracts and precincts to receive their pops and voters\n",
    "cutTractList = [-99999]\n",
    "cutPrecinctList = [-99999]\n",
    "tractReceivers = [-88888]\n",
    "precinctReceivers = [-88888]\n",
    "popToCut = 0.\n",
    "for t in range(nTracts):\n",
    "    x = tractGeom[t].centroid.x\n",
    "    y = tractGeom[t].centroid.y\n",
    "    if tractGeom[t].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedTract[t] = 1\n",
    "            popToCut += tractPop[t]\n",
    "            if cutTractList == [-99999]:\n",
    "                cutTractList = [t]\n",
    "            else:\n",
    "                cutTractList.append(t)\n",
    "            x,y = tractGeom[t].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        if tractGeom[t].distance(clipPoly) < 0.1:\n",
    "            if tractReceivers == [-88888]:\n",
    "                tractReceivers = [t]\n",
    "            else:\n",
    "                tractReceivers.append(t)\n",
    "xp,yp = clipPoly.exterior.xy\n",
    "plt.plot(xp,yp)\n",
    "\n",
    "for p in range(nPrecincts):\n",
    "    x = vtdGeom[p].centroid.x\n",
    "    y = vtdGeom[p].centroid.y\n",
    "    if vtdGeom[p].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedPrecinct[p] = 1\n",
    "            if cutPrecinctList == [-99999]:\n",
    "                cutPrecinctList = [p]\n",
    "            else:\n",
    "                cutPrecinctList.append(p)\n",
    "    else:\n",
    "        if vtdGeom[p].distance(clipPoly) < 0.1 :\n",
    "            if precinctReceivers == [-88888]:\n",
    "                precinctReceivers = [p]\n",
    "            else:\n",
    "                precinctReceivers.append(p)\n",
    "print(\"I have flagged \",len(cutTractList),\"tracts w pop\",popToCut,\" to reassign to tracts...\")\n",
    "print(tractReceivers)\n",
    "print(\"I have flagged \",len(cutPrecinctList),\"precincts to reassign to precincts...\")\n",
    "print(precinctReceivers)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d7009c9a-d5ff-4570-855d-1fb439afc32c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 2 of 4\n",
    "#visualize the to-be-cut precincts, just to confirm there are really xx precincts up there\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] ==1:\n",
    "        if notPolyVTD[p]==1:\n",
    "            for geom in vtdGeom[p].geoms:\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y)\n",
    "        else:\n",
    "            x,y = vtdGeom[p].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0218d8e6-7832-479f-b44f-b37c117ee40c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 3 of 4\n",
    "#now visualize the receivers\n",
    "for pp in range(len(precinctReceivers)):\n",
    "    p = precinctReceivers[pp]\n",
    "    if notPolyVTD[p]==1:\n",
    "        for geom in vtdGeom[p].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = vtdGeom[p].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5d698e51-7b2d-40b8-b932-d22f20f09bb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now visualize the TRACT receivers\n",
    "for tt in range(len(tractReceivers)):\n",
    "    t = tractReceivers[tt]\n",
    "    if notPoly[t]==1:\n",
    "        for geom in tractGeom[t].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = tractGeom[t].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c17c1582-29d1-41bc-8f56-b32ac70747ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have finished slicing  201388.0 people (VAP of 159890.0 ) and  89090.0 voters out.\n"
     ]
    }
   ],
   "source": [
    "#now, cut and redistribute\n",
    "cutPop = 0.\n",
    "cutVAP = 0.\n",
    "cutHisp = 0.\n",
    "cutBlack = 0.\n",
    "cutTrump = 0.\n",
    "cutBiden = 0.\n",
    "\n",
    "for ct in range(len(cutTractList)):\n",
    "    t = cutTractList[ct]\n",
    "    cutPop += tractPop[t]  #sum up the cut pops\n",
    "    cutVAP += tractVAP[t]\n",
    "    cutHisp += tractHisp[t]\n",
    "    cutBlack += tractBlack[t]\n",
    "    # Now we zero out the pops in the cut tracts\n",
    "    tractPop[t] = 0\n",
    "    tractVAP[t] = 0\n",
    "    tractHisp[t] = 0\n",
    "    tractBlack[t] = 0\n",
    "# Now distribute the pops among the receivers\n",
    "ntR = len(tractReceivers)\n",
    "NTR = float(ntR)\n",
    "for rt in range(ntR) :\n",
    "    t = tractReceivers[rt]\n",
    "    tractPop[t] += cutPop/NTR\n",
    "    tractVAP[t] += cutVAP/NTR\n",
    "    tractHisp[t] += cutHisp/NTR\n",
    "    tractBlack[t] += cutBlack/NTR\n",
    "\n",
    "#             now do the same for the precincts\n",
    "for cp in range(len(cutPrecinctList)):\n",
    "    p = cutPrecinctList[cp]\n",
    "    cutTrump += vtdTrump[p]\n",
    "    cutBiden += vtdBiden[p]\n",
    "        # Now we zero out the pops in the cut precincts\n",
    "    vtdTrump[p] = 0\n",
    "    vtdBiden[p] = 0\n",
    "\n",
    "# Now distribute the pops among the receivers\n",
    "npR = len(precinctReceivers)\n",
    "NPR = float(npR)\n",
    "for rp in range(npR) :\n",
    "    p = precinctReceivers[rp]\n",
    "    vtdTrump[p] += cutTrump/NPR\n",
    "    vtdBiden[p] += cutBiden/NPR\n",
    "print(\"I have finished slicing \",cutPop,\"people (VAP of\",cutVAP,\") and \",cutBiden+cutTrump,\"voters out.\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "55419803-eaaf-4181-8848-d8fdad0205b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Now, repeat for southern central tip\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y)\n",
    "pt1 = Point(-76.2,37.8)\n",
    "pt2 = Point(-76.4,38.6)\n",
    "pt3 = Point(-77.4,38.62)\n",
    "pt4 = Point(-78, 37.8)\n",
    "clipPoly = Polygon([pt1,pt2,pt3,pt4])  #LEFTOVER FROM INDIANA. plan to clip off SW corner near Evansville\n",
    "xc,yc = clipPoly.exterior.xy\n",
    "plt.plot(xc,yc)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2a56b3e5-803d-425d-8bb6-abfba9aa9716",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have flagged  142 tracts w pop 263227.0  to reassign to tracts...\n",
      "[19, 20, 22, 23, 29, 30, 344, 345, 458, 648, 651, 652, 688, 689, 690, 709, 710, 712, 713, 715, 717, 718, 732, 764, 767, 885, 887, 941, 942, 952, 964, 965, 986, 990, 992, 993, 994, 996, 1000, 1446, 1447, 1448, 1495, 1496, 2093, 2156, 2777, 2779, 2872, 2873, 2874, 2895, 3144, 3246, 3287, 3288, 3438, 3441, 4029]\n",
      "I have flagged  75 precincts to reassign to precincts...\n",
      "[297, 300, 340, 347, 348, 349, 368, 433, 436, 481, 526, 546, 757, 759, 760, 762, 773, 775, 776, 777, 778, 783, 784, 786, 788, 1669, 1671, 1672, 1676, 1677, 1678, 1680, 1689, 1700]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#find all tracts and precincts centered in this clipPoly, flag for cutting\n",
    "# and, ID all nearby tracts and precincts to receive their pops and voters\n",
    "cutTractList = [-99999]\n",
    "cutPrecinctList = [-99999]\n",
    "tractReceivers = [-88888]\n",
    "precinctReceivers = [-88888]\n",
    "popToCut = 0.\n",
    "for t in range(nTracts):\n",
    "    x = tractGeom[t].centroid.x\n",
    "    y = tractGeom[t].centroid.y\n",
    "    if tractGeom[t].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedTract[t] = 1\n",
    "            popToCut += tractPop[t]\n",
    "            if cutTractList == [-99999]:\n",
    "                cutTractList = [t]\n",
    "            else:\n",
    "                cutTractList.append(t)\n",
    "            x,y = tractGeom[t].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        if tractGeom[t].distance(clipPoly) < 0.1 and tractGeom[t].centroid.x < -76.5:\n",
    "            if tractReceivers == [-88888]:\n",
    "                tractReceivers = [t]\n",
    "            else:\n",
    "                tractReceivers.append(t)\n",
    "xp,yp = clipPoly.exterior.xy\n",
    "plt.plot(xp,yp)\n",
    "\n",
    "for p in range(nPrecincts):\n",
    "    x = vtdGeom[p].centroid.x\n",
    "    y = vtdGeom[p].centroid.y\n",
    "    if vtdGeom[p].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedPrecinct[p] = 1\n",
    "            if cutPrecinctList == [-99999]:\n",
    "                cutPrecinctList = [p]\n",
    "            else:\n",
    "                cutPrecinctList.append(p)\n",
    "    else:\n",
    "        if vtdGeom[p].distance(clipPoly) < 0.1 and vtdGeom[p].centroid.x < -76.5:\n",
    "            if precinctReceivers == [-88888]:\n",
    "                precinctReceivers = [p]\n",
    "            else:\n",
    "                precinctReceivers.append(p)\n",
    "print(\"I have flagged \",len(cutTractList),\"tracts w pop\",popToCut,\" to reassign to tracts...\")\n",
    "print(tractReceivers)\n",
    "print(\"I have flagged \",len(cutPrecinctList),\"precincts to reassign to precincts...\")\n",
    "print(precinctReceivers)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "264c0a29-0366-433f-ae45-63b986566b34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rb9lb2LbtSiorFWum8opflE3fjvcTE33JSZ91mBRvL17tEkduk513C1r8Oo3oFIrRIXnnQMvfT2lhFQA9B6S09OGrr3hnznsAdK5rWRlIJKGGu7Dq9YxeeQujV95Cv312xsWPU7/+VVROQPsRAEID4amtkpoaG3EtvJ8afOh2wYNtVsvO3IWLFtWRVleCy9UbjZiKwbCbnbvOZe3ah7lo0EfEdfuAhmo/BkU6qNm3olU7vmNTyDUUkqUvINkvitemJRGpqUOLiwhNPeFeAWwqrKGpzk1sjJHxDcoEGV1dxqRdG6ivKKNGb6bUsY9kTTB5PrlUxG5grC6YF5IvY8VlS7FrbTT62U7pdRzYUEqtTjJlQgecLjezf9rNHV+ls6uwltu+3M5TC5Uv8KgAE89PqaCxaT/pO65i167byMp6EKMxkpiY49v1H48LI4IYFeTLa4dKsTiVFUZ8sJnLtGZ2+sHbC/bhcrqxWBTdv8mjevr20Uf5KTMTL7udSWX++LhbNpMFgnmublwy/mGaIpMAWNlDMCr25CsTFZX2TPv5PJLuVucAsjb8jNeSe0h1F7Ctx8P09jtW979hdw7ejmp8EvswZvSjOBxWNBodWuUEFeXlN7Nx0w3A59jt95IcPoKYId+wYdME9uy4k0HJm9HqjDjtNu7Y/hCbknLxcnrxZvRL9BmQxgV786g/sI1DrgAemjaCO75ZQheRxyNnX0p9Uy1LDlbQs17xnu1O7s2K3DpqhD8/xL/DpR0vYdaQOc19/enH1RhdXmhKjKz+ci++wV4EhnsR1TEQw1Hmm7kHqvGud9HYxQ+9Tsu6nArmrMsF4Pvthc3lzu4eyb/GJNM5Ygxu9wxyc98g79A7uN02wsMmH7MPcqrckxjBpK3ZvFdQzu0Jil+gGwbE8/7mLObU1ZL0aSb78nYDsGjZPHqkprLL7SbJamXGY4/ww3nnEt6z9YG+H6PrmXMohLAB9+LAyU/ZOsImn/jQn4pKe6fdCQCX3cr6Fy9jaNNSigll58j36T3ygjarfDd/CTqp5ZKzhwOg15ta5YeGJhMaOg2L5WVKSrYRFzcUr4iOBGunssJ3PjWvjEakXsrne78gI6AGgCZdE1sy15IyJBWtRtmTuPXSaWgEvJJ1ERghf0sBX/pOJDT/ZjYDBWGRJKYm8mnYyzilA4C4oNZ29fszSjESjLtBw86VhRyNRivQ6TXoTToarU7cSOw2F3Pn7sXLV0+UU0NquC+BCb58tVXRpZ/bK5rOEYpZpkZjoEOH24mLu56mpjzM5oTf9NcA0NvPm/HBfrx2qIxkbxP7G2w8fbAYgIMRejbN34/dVwlBUVBQQEGB0p+ILVtwHMghz09HXeEcoswdKTZV8WraL3Sx24j2LGj1nn/WTTYnbRvAqqioQHsSALY62PgmlnWfMFRr4X3nRAIG34Q3ZtZ/tYTSmgZqrU5sLsHI1Bh6pybh1ViKMaYrUSHHN6W0WhU3DFLnQ3rxGvZVpvOJZRW5ViME10HxmwRp4EprXz41bCfOEkdxbSMvPPoI02bezjfZ2/n6p4WIIYnN/jbCtr7O0oR5zc+IN8SzoHQBTulgfPx44v3iuTiltd88fbAbV5GTGY/2w1LkJGd7GUX7a/EP8aKp3o6t0Ynd6qSp3o50KfsIAQcaKTmgOHi7FCNY7KSE6Rl/eR8OVjUyvNOxNvs6nTe+vl1/398FcF+HSCZtzebaXbkABOm1VDkUlZA+3hd7pQAh0ev1XHrppRSuX4//199Q8a6yB1BtL6XaXsqCQcUEFEWyte4ahoo61tISmdQ/zv+Y56qoqLTQbgRAjcObAH0DLnRspzcv2M+nYbUFOBzszOD5wbzllfRbvZ1uOkho5RvnWL46tIc1LjPkt7g20iK5KCSV2l0ljEqZytgLZ/LBqtdxlW1hWuQoCg8V4PL2xVZ6AH10CpqiTFYt2UkHvCg65x1mps9qbsvf4M/Ll7zM13u/JnNrJhMTJ5JRnsHeqr2tDjeZ/YxYpQ5hcJPYM5TEnieOneByuimpamLlnExqDihuFXQmLVnrS+iq03DdpSknrP976eLjxbbBXclusFLpcJJoNjJy014ArrpxGF72/uzatQt/f38SEhJISEgg66mnkQ2N3PXVfCq2b+X7l95g3Bo303Yf5MJJEgwQ/fgQmnZXYEo5dkNfRUWlNe1GAHxWOJDUUeMYceVNBAGLisuZvyYDf18ziVEhxEeHERLgS0ODhZ9WbmdneiNI2Jexme3hfvQachxXxlWB4J8PQIjGm2eG/ovOoYPx80loFSHh50OL8cfIVZf9i3fzXqC4voHlS3/hoUce4+E3XqOmDK4P6UzmzgfgiJO9I2JHYNabOa/TeXy4+0PuWHmH8lhrFY8Pfby5nMOu2PL7eJ1YYB1Gq9MQHeZN3aF6QmJ9cDnc1JQ1AVBT8tvcPp8ugXod/QMU/0VSSsYE+eGt0xBq0INBz8CBA1uVFyYThtgYig7sJGf3crpcOpTaFxdR5hfK2P4d2ZJXjdBpMPcM+1P6r6Lyd6fdCACXW4PUtGiEYyNDuWn6mGPKGfz9uPKcEXDOCIoO5vDe+x+wcMlSug8YhE531Gbq1n2EVSXzTMCFLGIhy93rCDcOVCb/I8g5tIODxnImin5otFr69O/P/GUraBRa1vyyGFttAW6tm1yv+mP6s6VkC063Ez+DH3MmzOH77O/5cPeHrClcg8PtQK9RTvDam1y4NTZMBtMxbZyIsDhfasubmDCzOwfSy7FUWxkwtcNptXEmEELwWc+2n+tyu6hpqMBdW0v151/A518QAlQFdCY37V8QDwWbyqjVuZBSqkFgVFROkXZjBvpbJoaoxCT6pXbDLjSsmD/vmPw1y1djQMfIc8dzff8bAXhr7evHlFv46+Mg4OqDC2nI20nfYSP49913oXU6WLZ2PSabicHjB7Pp2m3cHzGyVd2ihiIaHA0AJPoncmffO3lh5AtUWau4YekNVDRVAMomr95tJOvAgdMa48jLUtBoBPNeTScqOYAJM7sTGHFqq4g/msW5ixn65VDSPklj5NwxrE8R5KaGkH/dWVS/dB/bel7TXHZUo54b60y8ecsKcjMq/sJeq6j8fWhHAsDdymfOqTLu/AvROx1s2rYVt7vlPEDJvnxyLAWkxXTFy9fM/jLlZG4nc0fsDjtN1kYlRKTbzS9Ne0mz2ujgtqP5YCyOykOYfXzpmBiKcNoRhjqmDZgGwAVjXwBgclBPruh6BQ8NfAh/Y+vNzLFxY7l/wP1sLtnMpO8msa96H2PO6oNVb2HJs/tZsHrlKY3NbnWybXEeDbV2nHY3C9/aidXiOO139EfxRdYX1NpquaDTBdzZ9y76v/c1E79dw/i7X6LUEIlG+GCcXMYtb41m+G3dAcXYa/VX+/7inquo/D1oPwLA/dtUAzq9nl7dU3EIHd/N+bg5fdWCFWjRMHTKKNwOFw8feAKA5yvfoM/nfej/1QCGzRnCS/PvJUcrmZIwgZoBD+AlrOhf7U5lVQkHVi1Bl5/OzTf/GwC3dPPUpqcAmNz7Ju7pdw/TO08/pk9CCGakzOCjCR8BcO3ia1lZt4R+N4RRYyoj5wsHL737KQ01Nk4U8tNSZWPfxtJWaXWVTaf9jv4obkm7BbPOTEZ5BjNSZpAa0nKQ78DuUlzCwaVjpwHQvUsokR0VQTnxhu5/RXdVVP52tIs9AIfdhtvlRAiB2+HA7XSg8zrWgVlxxj6kXkdUl9a66ISYHmSvMlJS6scC/ToKMqtxVEWTGGnGz+PZ8rGYB9hRtgM/L38aXY0Y3QYW1y/ng+pFCCk5a8Cd+Ack0JTxFl62UnbcPxGjLYq+d91IaIByGGp90Xq+3vc14+LHMThq8EnH1Tu8Nx9P/JjnNj/HG+lvAGBINXHZ1kcwbI1iztZfGTGjE6kjYtqsb7e2dgI36abuhMb97/jO7xfRj/8M+g+z1szite2vsbl0Mxd2upALOl2AvUCHI6QKL1PLnkfx/loAQmJ9jtekiorKEZxUAAghTMBqFO/FOuBbKeXDQoiewFuAD0pM30ullMeEaRJC5KK4zXcBzsOBiYUQQcBXQIKn/nQpZfXvHlEblOceROdyU71qJenPvITBasU86990uOKq5jI5X3yG9ZHHsRp0bBt9C5NfuhFro5WFbyyjMFuPFj1oJbm/WgEvBFBfHERpRjnhPUKZOvYipnJRq+cmrezAg3lP0sPhwN9fObhlujeLwif6MCL8IPuiOjKy/xQAXk9/nbd2KL71b+116ymvVlKCUnjvrPfYXLKZPZV7CPYKJuGsRPZ8WUv5/kZWfbGP6tJG3C5JZaEFl8NNpwERhCf6UV+lRAobMaMTnQZEYDD9730PBHsp5pwf7VFWO09seIKpSVPRN5rRhFvarNNYZ8fb39hmnoqKSgun8j/eBoyWUlqEEHpgrRBiIfAqcLeUcpUQ4hrgHuCh47QxSkp59M7cLGCZlPIpIcQsz/29v20YJ6Y8ey9D9+Zj3nUQp1aDFILyDz8kqP9ASpcsxPLhx+jtdvSARgrC1nzL5w+FUFupxe32x+xTybS7R2Py9eb7t9fiG2ikV7eOzP8wk2/f2MmYs+NJmZJ0zHPTfOAGez1TS+upX/cIvkNm07D/a6JdB0DA2dfcD0BOTU7z5D+lwxQ6+J++FU6/iH70i+jXfJ96N1SXNLDi0ywylhegN2kJjvJGSlj7dXZzOSEgpkvQ/+TkD6AVrYPdOKWT6sYavBw+6AJa9isK9rZ8OxTuq6ZTv4g/rY8qKn9XTiUovKTltJTe85NAZ5SVAcBSYDHHFwBtcQ4w0nP9EbCSP0gAeDmcuBxOLBGhJL7+Bgfvugu/3EMUTzsXgMPKIGuvnhCWSt4hA9XlQSAb6TPRyMBpFza3deldY5uvB+yuYv2mUtYtOtSmAKgs/YVu8UYCLDZMK1+hTgh8liqbvLmxXuTULSOWXnyw6wMA/tXrX1zf4/ozNu7ACG/OvbM3lhobPoFGhBBIKakosFBV1EB1SQMRHfwJCDuxP/+/kl5hvbg57Wbey3gPu9vO+cnnU1KhfEt4Wf2wW53sXl3E+h9y8A020WVwJB1OcghORUVF4ZQ2gYUQWiFEOlAGLJVSbgR2AYedzl8IxB6nugSWCCG2CiFmHpEeLqUsBvD8+Yed3olN6gRAyuxHCemWSugNLd2oi4nEOnYUzrMnEnrF/6Fd/gNSo3x1hnbQM3DakOO2mzAiGoDIkGNt7+0NNdQY1hHkHo97xN3oHS78lryARsK2EVPJSTBTX/gWjQ4LO8p3kBKUckYn/8MIjcA3yNSsUhJCEBrrS+cBEQw8J4mE7iFn/JlnEp1Gx009b0Kv9Zx3cNn5OX0pAPUZWt69fTXrvttPh54hXPxQf/qdnYjOoD1RkyoqKh5Oad0vpXQBaUKIAOB7IUQqcA3wihDiP8BPgP041YdIKYuEEGHAUiFElpRy9XHKHoNHaMwEiIuLO9VqrXA3KCdbNZ6N34Rzz8cycBDSZsM3IREAl93BjqETERo99d7KxN5oqTxhu17BXsfNK9rxA1Jrxy+iOz5J51C34S309TXI6R+SHDmQTesGYJdwwbwLya8v4MmhT/6msbUX5kyYw1d7v2LhwYX4VoYzje4ERJqoKbbS7+wE+k1OVA+AqaicJqel+JVS1gghVgITpJTPAeMBhBCdaOX4oFWdIs+fZUKI74H+KKqjUiFEpJSyWAgRibK6aKv+O8A7AH379j2+TeOJ+u1SrF2EvmW4PpFRrcrsf/ljvOoK2dnteuInCDS6KjKXR1Ccl01kfNsxZRs9JpPCEyvXbXVStyQPY3IAefWvgh4OlD8NPjYSb27Ru8/ffg9ewE81eqqdNQyPGc7ExIltPULFQ0pQCg8Peph/9/s3uyp24XthAJ3DO9JU78Dsp/r8VFH5LZxUBSSECPV8+SOE8ALGAlmeL3qEEBrgQRSLoKPregshfA9fowiMXZ7sn4ArPddXAj/+rpGciJOIDVuthcZP3qPWLxF3/0BGX3whbpfimVKjafur0tnkZNEr6WiAQVcqTtmadldiWVdE5Ud78Msf2lz2wMGXyMl5DrdbWSQdPk5ml4Jbe93K62NeVyNXnSJeOi/6RfQjJSIZIYQ6+auo/A5OZQ8gElghhMgANqPsAcwHZggh9gFZQBHwIYAQIkoI8bOnbjiK1dAOYBOwQEq5yJP3FDBOCJENjPPc/8G0PZnvf/kTTPYacjqcw5SbzkWj0ZCXLvCNKCY8tmObdTLnH6DG5sYNbJ+7H1u9HVe1tTm/87D7Se74IB073gdAbt6b7Nx5C/kV6zBUfQdAmVPH6NjRZ3aIKioqKqfIqVgBZUCzq/oj018GXm4jvQiY5Lk+APQ8TruVwLHe2P4ANvyykWhg7axHCbz6KjoMG0BwTIuZoPXX1QhTMLYO/nj5+GGpK8JaG0JwbPFx20yZlIit0UnRvmp276vhwKxfcbklkXrBiIs7Ye4cThxXAxAZMY3i4u84cPBFqqrXoffIoZf7X0OkT+QfOXQVFRWV49Iu9A61WiPRQOyhLHhkFmXAlrBExi6fh7WiBtOhDPJjR9NUFUbRgb1EJnRE772e+ioXbrcbt9vB/Hnfkp7hQ1zfYK45eyh6bz19r1QCoxxckc+6nw5Q0+Qi3y5xGVu/VoMhhPj4mYSGnkVm1n3U1GwEIMRbnfxVVFT+OtqFAAi88AImNiQwZ0YqxgPZyEcfJK7sII31DeS+9hk66cY4uivkgkZrQqvTE5/mZP+vMcx54DsabTpEYyShQNM8O2+793LDlM7N7SeOiqWwwkrNsnwSe4bg37tti1azOZ5eaR+SvuNampoK8PdXY9aqqKj8dbQLZ3Auz65rcHAAg84dS37fEQBsevML5I8f0xCezO5GDXaNlcAwxS5+3KUXkjq+gqY6H2qFPwsGaSjt6U2jSeBcUMgTT66n2mLDYXPRWGdnxzIlKExsavAJzRE1GiO9e33KkMEr8fFu27pIRUVF5c+gXawA3B6PmBoNuN1ugjM2ARD43acIt52HpuRREPoaJMEH38LCc74k2r8rI86bTvigTMbtsnKx3w4eHXAVliYHL728hcDcJj75969oWzxE03dSAt2GRf8VQ1RRUVE5bdrFCqDZJbLbzc9nX0x0bQn58V0xWSqZ199JQaiboaLF2ue6RZcz6vMeXPvjKFIiuyB0DixSOe3r46XnwVmDiLy8I0WBLfIzsqM/vcb9toNqKioqKn8F7WIFcFgF5LY7STq4E4DYvD0ALO+huA1484rv2Zj3A4+sf5R8m+JkrLJG8TmTpCllt611lKygLgHMsfnSocTBK2ERdOkXjsGrXbxOFRWVfwjtYgVwWAXk5WMi4OelrfIMLkh1KV40B8RP472JnxPvseKJMih/BuscNEl9c5379xVw8Y4DIAQHIg2YB4aq7odVVFT+drQrASDcdjY+8SIOoaVmoOK5IsAi6eLTrblslH8K353/K+EGLaV2Bw22GpqkHiEVdxIVdicfFykrgwH+3tybGEEn8+kFYldRUVH5X6Bd6CwOC4CYZ5JxrA8jL6ErYXnZNJpD2BNXTSdd68hYBr2ZkeGpfJW/A73OTIDWSYYjmuey9/BWsROtECzt24kuPsd3BqeioqLyv077WAG4YapmHRotSAlxBzMxFe8jv08vXFpBrPnYzdsdVTkIJC5XI/8X7kBKwXMFdiwuN+eGBaqTv4qKyt+ediEA/Kt38orhNfTeLvwGhAOg0bvZ3UVxEz20y4BW5a9c/hBZDRYCfVJ4es8qcgs+YTb34ytruS7SzBPJqqmniorK3592oQIa1LAMgMrLlxOd1IeQmmpcT3Xny2DFJUPH6Pjmsl8c2MC2/B+wG7uQF/B/vFXtCzzGjdqPyB4x4q/ovoqKisofQrsQAN4+/gAEJ/UBwBgQSFHHy+hg/4EDBj19vhiOU+hA2hFuCxLBq8NnMy4yhXO3pLPBomGr+coTPUJFRUXlb0e7EABIN2j0rZIirnqM89/4lWcNNbjcdcSGjsegNWDSGhkU2Y/xUV3ZWGNhg0XRkr3Rvetf0XMVFRWVP4x2IgBcIFpvd2h0ejr4d+D2qmWE9ZjJlDH/Oaba3NJqfLUatg7uhp9OjTOroqLyz6KdCAA3aFpP4BtXf8DQgz+g734DA46Y/DfUWHjqQDFxXgZ+Kquhj5+3OvmrqKj8I2kfAsDtbrUCKCrNoduqB9gT1JO+055oTn/uYAnP5ZYAsKG2AYDJYQF/aldVVFRU/izahwCQLhDKV7zb7aLqm+vxQ+J/4XtotcreQI3D2Tz5rx/QBbNWQ77VTi8/81/WbRUVFZU/knYiANyKL2hgw+IXGFyxlQ0j/svAyE4ArKqq572CcgDuiA8n0az49Qk36ttuT0VFReUfwEkPggkhTEKITUKIHUKI3UKIRzzpPYUQ64UQO4UQ84QQfm3UjRVCrBBCZHrq3nZE3mwhRKEQIt3zm3Rmh3YE0g1N1ZRX5tNzy/NsjxjGgBE3AvBqXikX7chhZVU9t8SF8e/EiJM0pqKiovLP4FRWADZgtJTSIoTQA2uFEAuBV4G7pZSrhBDXAPcADx1V1wncJaXcJoTwBbYKIZZKKfd48l+UUj53hsZyfOqKADiw4D/0cjsImfIsQqPhQKONFzxqn6yhqXirm70qKirtiJOuAKSCxXOr9/wk0BlY7UlfCpzfRt1iKeU2z3U9kAn8+X4UwlMB6HvgB7Z0voTY6C5IKbl7bz5Nbsma/inq5K+iotLuOCVfQEIIrRAiHSgDlkopNwK7gKmeIhcCsSdpIwHoBWw8IvlWIUSGEOIDIUTgcerNFEJsEUJsKS8vP5XuHot0AWDRmekySTH5vDXzEOtqLIwM9CXZW3XnrKKi0v44JQEgpXRJKdOAGKC/ECIVuAa4RQixFfAF7MerL4TwAeYCt0sp6zzJbwJJQBpQDDx/nGe/I6XsK6XsGxoaekqDOprq3M0A7O51K4F+oayuqmduaTWDArz5pEeH39SmioqKyt+d0/IGKqWsAVYCE6SUWVLK8VLKPsAXQE5bdTz7BnOBz6SU3x3RVqlHsLiBd4H+v20IJ+eXhHPJ8OlMn3G3U2S1M32H0tXnO8eh14g/6rEqKioq/9OcihVQqBAiwHPtBYwFsoQQYZ40DfAg8FYbdQXwPpAppXzhqLzII27PRVEp/SFsjR3PxL7v8EmZhd7rlf3nRztG0cGshnFUUVFpv5zKCiASWCGEyAA2o+wBzAdmCCH2AVlAEfAhgBAiSgjxs6fuEOByYHQb5p7PeExIM4BRwB1nblitmVNYgUvCg9mFADyUFMXM2LA/6nEqKioqfwuE9IRL/DvQt29fuWXLltOuN7+shut253J+eCCvdYlDWZioqKiotA+EEFullH2PTm8XJ4EnhwVQEpb2V3dDRUVF5X+KdhESUkVFRUXlWFQBoKKiotJOUQWAioqKSjtFFQAqKioq7RRVAKioqKi0U1QBoKKiotJOUQWAioqKSjtFFQAqKioq7RRVAKioqKi0U1QBoKKiotJOUQWAioqKSjtFFQAqKioq7RRVAKioqKi0U1QBoKKiotJOUQWAioqKSjtFFQAqKioq7RRVAKioqKi0U04lKLxJCLFJCLFDCLFbCPGIJ72nEGK9J67vPCGE33HqTxBC7BVC7BdCzDoiPUgIsVQIke35M/DMDUtFRUVF5WScygrABoyWUvYE0oAJQoiBwHvALClld+B74J6jKwohtMDrwESgK0og+a6e7FnAMillMrDMc6+ioqKi8idxUgEgFSyeW73nJ4HOwGpP+lLg/Daq9wf2SykPSCntwJfAOZ68c4CPPNcfAdN+ywBUVFRUVH4bp7QHIITQCiHSgTJgqZRyI7ALmOopciEQ20bVaCD/iPsCTxpAuJSyGMDzZ9hxnj1TCLFFCLGlvLz8VLqroqKionIKnJIAkFK6pJRpQAzQXwiRClwD3CKE2Ar4AvY2qoq2mjudDkop35FS9pVS9g0NDT2dqioqKioqJ+C0rICklDXASmCClDJLSjleStkH+ALIaaNKAa1XBjFAkee6VAgRCeD5s+z0uq6ioqKi8ns4FSugUCFEgOfaCxgLZAkhwjxpGuBB4K02qm8GkoUQiUIIA3Ax8JMn7yfgSs/1lcCPv2McKioqKiqnyamsACKBFUKIDJQJfamUcj6KRc8+IAvlq/5DACFElBDiZwAppRO4FVgMZAJfSyl3e9p9ChgnhMgGxnnuVVRUVFT+JISUp6WS/0vp27ev3LJly1/dDRUVFZW/FUKIrVLKvkenqyeBVVRUVNopqgBQUVFRaaeoAkBFRUWlnaIKABUVFZV2iioAVFRUVNopqgBQUVFRaaeoAkBFRUWlnaIKABUVFZV2iioAVFRUVNopqgBQUVFRaaeoAkBFRUWlnaIKABUVFZV2iioAVFRUVNopqgBQUVFRaaeoAkBFRUWlnaIKABUVFZV2iioAVFRUVNopqgBQUVFRaaecSlB4kxBikxBihxBitxDiEU96mhBigxAiXQixRQjRv426nT35h391QojbPXmzhRCFR+RNOuOjU1FRUVE5LrpTKGMDRkspLUIIPbBWCLEQeBR4REq50DN5PwOMPLKilHIvkAYghNAChcD3RxR5UUr53O8ehYqKiorKaXNSASCVqPEWz63e85Oen58n3R8oOklTY4AcKWXeb+uqioqKisqZ5JT2AIQQWiFEOlAGLJVSbgRuB54VQuQDzwH3naSZi4Evjkq7VQiRIYT4QAgReJxnz/SomLaUl5efSndVVFT+5pQ0lPDkxifJrc39q7vyj0YoH/inWFiIABQVzv8BM4FVUsq5QojpwEwp5djj1DOgrBC6SSlLPWnhQAXKSuIxIFJKec2Jnt+3b1+5ZcuWU+6viorK3w+r00q/z/o1339w1gf0i+h3ghoqJ0MIsVVK2ffo9NOyApJS1gArgQnAlcB3nqxvgGM2gY9gIrDt8OTvaatUSumSUrqBd09SX0VFpZ0ghGh1H2AM+Gs60g44FSugUM+XP0IIL2AskIXyRT/CU2w0kH2CZmZwlPpHCBF5xO25wK5T7rWKiso/DpfbRZ29jhWHVgBwXvJ5AKwrWvdXdusfzalYAUUCH3mseDTA11LK+UKIGuBlIYQOsKKohBBCRAHvSSknee7NwDjghqPafUYIkYaiAsptI19FRaUd8eaON3k7420ADBoDt/e+nSJLEc9teY6zO5xNiFfIX9zDfx4nXQFIKTOklL2klD2klKlSykc96WullH2klD2llAOklFs96UWHJ3/PfaOUMlhKWXtUu5dLKbt72p0qpSw+04NTUVH5+3B4gh8QOYD5584n0BTI4KjBAFyy4BKKLa2niBWlVfw4biIr3/3wT+/rPwX1JLCKyp9MZcEhlrzzKp8/dDdWi+XkFf7hSCl5ZdsrPLHxCQCSA5KJ9FE0xGclnAVAcUMx4+eOb66TXtfIY/N/oVN+LuHPP3Pctm02Gzk5Objd7j9wBH9fVAGgovInM+/Fp9i5bDHF+7LI2brxr+7OX4rD7eCtjLd4d+e7ADw34jlu73N7c36UTxTzps1rvt9VsZuL03OYsHUfvfbubk531bZSMDTz/fff88knn7B8+fI/ZgB/c1QBoKLyJ9Nz3MTm66J9mX9hT/5asquz6f1Jb95If4MgUxCbL93MWQlnYdQaW5VL8E/gtt63ATB1zResrK4HIH346OYy+wYMRLpcreo5HA727t0LwLp16ygqOtlZ1faHKgBUVP5keo6bRFB0LACFWXv+4t78+TjdTh5b/xjn/aRY+UzuMJlfLvgFk8503DozUmbgFmYaAy4A4NcBKSybPALmftdcpm7RoubrqqoqPvjgA6SUTJs2DbPZzEcffURlZeUfNKq/J6oAUFH5k9FotfQYMwFQ9gOa6uv+4h79ueTU5PD1vq8BmDt1Lk8OfRKndJ6wjkAghb75PsmsCIsu3bqwc6Ry/rT8LcWCqKmpiTlz5lBcXExUVBQ9evTg2muvRQjBDz/8oO4HHIEqAFRU/gISe/Vpvm5vq4BOgZ0I9QplYORAws3h9Pi4B/0/63+MlQ9Ao6ORTcWbGP7pSJyGBAASvQytymg9qh9HdjZ7L5/JN/fMp77awhVXXMHMmTPRaDQEBgZy1llnkZ+fz/bt2//wMf5dUAWAisqfiNVqZfbs2Rwqq0CjVY7hFGTu/It71YLF/sdbJQkhGBQ1iA3FGxj65dDm9BDzsXb+Cw8u5NPXl3PVhsfQaYYD0Ln0EDe9P4f8EsWxQNTVVzaX39GUQr0zGP/aKBISElq1lZaWBsC8efNQ/YopqAJAReVPwu12M2+eYtHyww8/YDeZATi0K+O4dZwVFUi7/ZTar/slj4qPdmPLbdsiBsBtc1L3Sx61i3KPyXtv53sM+mIQl/58KTXWmlN65tFIKVny/m52riwgZ3sZr9+4nM8f2dic98WjG3n9xuVMaJzRvLEL0P2gm7lPXkedvbU6bGLiRJxaB03OLfTcW47ebmORXwTfd0ijX0Yhz/y6j4Brrmoub4iMACAoOgqNpvX0JoRgyJAhgLJHoHJqJ4FVVFR+J263m6VLl7J79xGmiyYzPfv3YfeKFVQU7yAksmerOrbsbA5MmQpAx1WrEKFBXPnBlXSu7EyIbwjdu3dHSolWq6WwsBBLVjlpTfGEZVbhPSiSwHM6NrflrLFR8swmNN4G3PWKQLHm1BB+SxoACw4s4OVtLwOQUZ7BFYuu4KdpP53+QCVkby4le3Oz2y+qixtoqrez+st9VBU1KM/4roxb3rqOyqZKPt3zCQ996QY28drYV7l/4APNdc16M7k91+NvN9B3p5PeuzaQdv4lXJsTgn1AKL/s3soUT9mDyZ2x2PUgwD/Up83uxcfH8+uvv+Lt7X36Y/sHoq4AVFT+IKSUHDowh6eWzyBqVQaLtu9ozgvSSG557CmMAYrKZfOm6WzefC7LliexceM0vv9+KkseerC5/P4RI1j0zhd4VXnhblI2MZctW8by5ctZunQpBQUFFLgr2OiVgyHeD1t2Tau+NG4vBTdovfUEX9YFAEdBPVJK9lTuYdaaWYSZw3hy6JNEekdS3vjbVCRCI9pM/+CetezfWqb0xSDwj/Tm25IqLupyHQjBvAeG89XZvnyx90u6f9SdkoaS5rr59fn45ylCSyMljfl56Kqb0O+sIqNjCtc8+DTF4ZFYffyoFcEABEf5HdsJwGBQ9g/sp7iq+qejrgBUVP4gCgo/ITv3MX7iKQCWp/Th7J3rmTJ4AIPGjAOg/FAOWoOLyIQRuN1NAFgaduLnD7WG1t57m8r2YdaZsWvs3HzzzTQ1NaHT6WhqauLggnS+r1uO0Ap0oV5Yq6yt6jpKGgHw6hmKV2oIPsOjsawrZnPJZm5feTu+Bl8+n/Q54d7hLDu0jLy63x63adhFnVjz1b42874c6kN2tAFft6A+8xCTQvzZdtk2dBodpec00LD9ERblLuKKhVfw83k/c9WiqwAwCEDrRroEXt0H4S6zoClqgu5BVPoHEFpehmXYCMXBPLBjeS5DJ6Qd83yjUTljYLPZfvP4/kmoKwAVlT8At9tOXu5bAIShqENK/YP5YOhkmswt6ommunrcTg1J8Q/Tu9cn9O3zLS5XMFarP2e/PYdDURE0enuRfF4xte4SguxBBEsfpJR4eXnx0+ef8Ppzz/JD1nL83WamX3ExWj8D7gY70tFyMMqUEqT82VmJu6T1N7JZl8W1S66l3l5Po60Jq0UxxaxoqiDYFPybx57UO7TNdKcGsqOVL/B6jRKH5OeKWt7NKiXxvp8Z+OQq4lwzAcX1Q69PerGjbAfeTVryz0mm6aog0m7IQqNRwoYI4KFNFoZv34zO7cJYWk3y/m8BCIrwarMP6gqgNaoAUFH5Aygs+pKcSg3fl01hgxjaKu+DwgoOB2IaOv16JPDV4zfhsDeg0cSh1Vai0/bA6OXFiHnzyRqTzH5DIgVEM8hUzR0PzkIIQU7mbnYePITT7SbVFcdQmUhQbBj6CG9wg6O8qfmZLs+KQB9mpqq2nieXfs5LYV+15AsnkxdNoO8HfdlRvgNfg+9vGvcbNy1nzr2/tpmnOSL2VE9fL9IHdyNCq+WZT1pUY88v2cdLI19qvp+6NpILV8QQMzcH0/s1OC0huKWR/w59lFeu70oft5Z+TX64NFri1iwmpmAF+9xLmHHnWW32QRUArVEFgIrKGaapqYC9ex/hP5vvZ376OLQH65kY4k/G4G4Mt5TzS3Asr65aC0BSj0n0PX8Atfludq79nG3bXgdAyv0AmLy9iZl0I19yDhpcjL39CTRa5b/tj99+C24X1152NQOcyXhFKl+9Wj9lknPVt0xy7gYHAAdfS+eLezcTU9KHQXnTmF48lTXn/spEvXLC1qZVVCP24tO3kik/VM+JAgwKtyS4TlmV7KhvIm3dbmYlRbYqY8CB5VMLzw1+jhElafjoY1vl7/oslII1gegNLiZ3SMA8KZEB+i74jXmMpoBYBJAUEUbtobbDixwWAKoKSEHdA1BROcPU1GykXIRjGxaBYUMZXiYdH3ZPBOCTiaPou2AV72u03ORyo9dq8A8PU+rVZiP95wIgZRjz3vsIs72RDdVVCGBszzi0Jh/sNhvfvP8OdS5JbIA/+gaBDTCEemP99lU0Ge8TYzqIZe+30FnZa2hML6POJVmRVdPcz14+ZUyb+SBGPy+envEfet4cR6BWsFg7n4Fdkk973L7BJsz+BhprW39du1212OveR2MaQJXP5Ob02+PDGe+t566OvmiLGtE0uvjK8CjRjhre+OwKQkjCFg4mvY47XnmbJZ/ewc552byTdDtWuze58/fS1d+Lg2V2vLX+iN7/Rh/wAf/SPILrg8ew3ZmN0a/12QKXQ+mbva7stMf3T0RdAaionCFsbjcRK9LplZXEHeINMGiwD49g2bm9m8sYdTqu9dFR6hPAG6vXYrfZWPHuzwCU6pRVwe59k8jY3JOtBQdZU1aKw+EguVMnBp97PQCfvPka2WWKT5uYuDg2fP4N3tqfidj9CqZdD2LQHATAZ9sF8P2N4LQTOrMH6y0t7hZ6jY7hoodmU11Tw/t3fc2bN/9AN6MPPXyS6OrwJ9B9+maSJm89Vz89FI2utSWQ0PiwYtDZPH3VFOQRVkL3JEZQUNeAK8kP+7AIbit/lV6aHGpprX6q81X2L8Zf9iIXP/ssubHJlHhH8anDQs4XOQA0uKDcpOcSwwIAtLgp+e5Bjmb9N68AELXpsdMe3z8RVQCoqJwhrt55sPn6wohAxgf7sWlgF+K9jDhcbv5vXTaLD1Zw87BBADxvM/DstZfgtrnxS6gnzLuU/PxuVJUE0+SWRJgb6Np1BTqdjSFd45BSMu/zT8ivriNAr2VQj27ERHTkUNFmAvVv4MdybEFTkQ+UwSWKrx12fAGPhyLMVqSXDrO/gRtfG8ng6Z0AWPDar1gbQtAb3RR18sHv7h5otfwufzl9JyY0Xw/10TIsyEh94rEhv6NX7mB8TsuX+LxLL+W1ISMoTTxKj6Rrcf2wqqi++bokUMd3vYswXBhN3FXJnHtHGuXeLcHjDaVbm6/dLicZaxfxa56VFPbTSad6BgVVBaSicsZYXtUyOb3aJb5V3mMZh/jG1sA3By2E73KDjxb/Bht7vFIojI1lzqyzueSnKczIGQGaeoSE5L6Kp0u/0jLy0lewbdcBduw/gLcGrr7pZvyDgnnrpgexum1kWbqS4rMH2/nPYNQbodNZcH8xPKno2Mvfuw1b4zX4hBr47OefCArxI2dPMZrGMIr803nwkevxNilf3hqdHrfzxM7ZTsSRh8A0gL8bPlvfyEPdTSyM0h+33h7RnT267lyV8A22whr87QEADHV2pXjDHiIHduXLygowRzXXORQZR+07dzHoptkkxcZSdcmnLHx3JiWEUdvkQ+RXjxAdnczmZVuolX7EUcw0FrMp7HIG/uYR/nNQVwAqKmeCkp18tvuBVkn1NitWu2J982VRJcLhxrfJTamPFq8mF79MHMjW5NEU+3ckwT+WereLc+39udo6imttY/ArGoyxIpGqyhh2FtSzY/8BdA47//r3vfgHKWaaQqvsH2wqVybvmkVvtXTAYIbZtTDmYSJq55HovQhLuZ36n/3I+xh0WyKp9ilhffwSXlzwaEs1k4kmS4swOykb34bHw8GunDUYMLUDABfe0YtAnQaNUNQ+j+20smVxPYtWtO1vaJQxnwH6POa4L8QaFEeo24/egV3p7IqiZtshdq3/gF5eyh5JJ3sWm/om8ci2dASw/lvFoskQ4sO/Ew/yQuJGagggM1Pyyy/7qJV+TOYXLuN75unGkB846NTH9w/mpAJACGESQmwSQuwQQuwWQjziSU8TQmwQQqQLIbYIIY5d4ynlcoUQOw+XOyI9SAixVAiR7fkz8MwNS0XlT2btS4yp2cKM6jUA9Fn8C8nrshi8fA0llYXUeWnoJXXM6ZVEl0b4sX8n9lRYsFZZSUtWdNxCF8Tr4V+SbcrD6vU0vbJ/ZciezZhlE+VOM3qXg/+7+26MXubmx1788HX4hvai3GqgqMmXuPyXWPvtK2yd/w671/xIVcFeGHYnQm/CGvEpP6e8jXV8NrJ3Od5j6njo2cvw1Vj5rn4hDVZlYo7q1IWivZk4HY5TGnrmlS+R+WkQVCn6+KTeYdz8xijsc9q2xAmxS25Y9QMDDuzGx9pIdH01ACtssaS6AokUVaxIHMk59n70LlZWMLraIkqbnmA4K3lJ3sicGH8yl5xPVE9lSvEOVFYFBVUtqp2g8n5EEkJC4jZiO2/hX/3uJW74L/zfkFnUnMBaqT1xKiogGzBaSmkRQuiBtUKIhcCjwCNSyoVCiEnAM8DI47QxSkpZcVTaLGCZlPIpIcQsz/29v2kUKip/JQ2VsOcH6D+T2/tfhNi2ln0YSbQdZK0xkbSMchCC3oHeDIkKYEVUGgAfpucDMCQxhAu278cR8zQfhdby6Lrp6CUgwIWGNH0B662JDOwQiH9gUKtH+4eaaahWol6VNvkQ5VXP0F0PtSqTHzqSWKeVN6JiqDUc4P/O+QSDtkWvPiOsK1N3fs6aV7ox7LJfiO/Ri+2L5lG0dw9xqa39E7VFoxHMNpBBydQ1OticW0UfXy84kUko8Fi/7nTu3JnKzEzuWr+VFSl9eN/tx4YwI49vOsDWwAD6VLux+eST37cl7m+KaTo1lbvRBeYgyUFn7khlcRCNDVZ89D50svWg0/6haF1euCsSiR68l+26NHw1Dq4zuHnPpuWNgFiuP+nI/vmcdAUgFQ6v2fSen/T8Djvc8AdOd1flHOAjz/VHwLTTrK+i8r9B7hpwO6HbecQHhfHC2POYf9bZfDvhXKZXbiLaWopPbSP390poVa3KrujZ7Q4Xa2ssVLtNaLVBaNDwVvyVMLsW7exqbFVNBOZsY/jFN7Sub7Hw31dexO1UVC9bqmL4umkoFddsZHm3J3FILQCx5SsBKNVqsLqsvLb9NQCk2822pfcyedcX+EjJhMYaTG/3Q/P99fjrm8jftu6Uhn/N7VouuUdL4n+W0fPRJVz38RaeeF3xACr0GnTBJgKmdMDrls4A7Ncofn58fX0xGo2EJCfTuTSf6ZuX4WWzcXZ2KfMTolkcVox0FFLa5ROktmU1IqSBgNCY5vuUC/PQ6GKZ85/leEsf5s78jG7eyvkBt9MLbZORdHt/dsgOLPPY/1tNLauo9swp7QEIIbRCiHSgDFgqpdwI3A48K4TIB54D7jtOdQksEUJsFULMPCI9XEpZDOD5M+w4z57pUTFtUX14q/xPcmg96M3g+bI/kgfS+rJ143Q+KJuLWa9tlTd/ZwnoBFf3iG5OW9zJDy1uOkUlASCdTnKKNTjcWn586VOaLI3szd7HF4sX89ptN2D4dRn1PfqTNeIcfuk6lqxJTxISl0JEzjdoaW3JM7Ve+Y77Zt83ABQXrKf3r2/h53ZTfN0SaqbPpyxkJIm6XK7ruIUhB2aB7eR7AQvP+5WaA61XHbejROySbknEPf3wGRJNWfohAOInpzJ48GCio5Vxa7XKe0kpLuBJg4MaHx/M1ibuHzOA2OcvxqbNBsBl0xConUnPIbdSVZrV/CynjCNlrB+ywcTnD23h9RuXU1usTPDxfT5m1NgVvD30GlKa8jmIGY10U+1So4LBKVoBSSldQJoQIgD4XgiRCswE7pBSzhVCTAfeB8a2UX2IlLJICBEGLBVCZEkpV59qB6WU7wDvAPTt21fV3Kn871GUDo5GsJSCf0yrrLAOaTRKIwbnsRNpUZkFv1AzH5a0nLr1diqTtLlJQ+7iX7BbLGi8xiPt28nbsYDXr1uEkMppWr3eSNo9jzCqd29ilm3HDTzfPRS324nWWoVGtP7vMrUimvWh3kT5Kr56GuoKAFgVnsSImAHYqwowTH+Jqv3rMS27D7O7GpnxNaLftSccflaxFelq7X55IXYmYoDLUprT7AX1uNEQ27cjiYYuzelak4mApibK/P25Oq0rE92SRpuNqOgoyrZswu2njMNXXkvvEYqWuLz6J4TGG71vAyavHIaN60aTZRt5G5QvfI2hjuEdH6HrFWsRRm98gTeSw7D9dAM/hI7mk4QZJxxTe+G0zECllDVCiJXABOBK4HBEh2+A945Tp8jzZ5kQ4nugP7AaKBVCREopi4UQkSirCxWVvx/BSZC/AV7spljdHIl04RR6nHVl1Fkd+JkUM8hfi2uw1drR6TS8tHQvItJMYqgPlTUHkdY+bF/UGTcawI9Abwfjnn2eb/49l1LzfvyHdCUpJpY+HTsSHxHOV/sO4dYpi/mhz2zHoLFzg+iHWysRZn+arHYOuMKY7bgSU/237LDuoMHRwMGCdSQDHbteQGNxNvKNoRiEnUprAAHGatCCWHAnlvoofEZPPO7wu0cHHJO2RDiZKA1kbCokpotyGlfotWgQuJ1uaB3VEbNGQw2w5ZtvGHbDDQQAbqeTT559lLC0UAJ1oYy5ZxYApYe2Y/Bvec9NTV3R6YxMvmoIWQOz+PrTHzCJIrpVHYBvLofpn4K1jq5J/cgymrmu8DvejbkAt5TNFkrtlZMKACFEKODwTP5eKF/5T6Po/EcAK4HRQHYbdb0BjZSy3nM9HmXzGOAnFCHylOfPH3/3aFRU/gr0Hn1yr8uOydry1kz6YSHLYuKy2Ut4+vzu9IkP5NKXFYdpDZVWdJWgz7EQF+1g/R4T8CBBujy69tTS1CjpPKYXO3Nr0ehjSLtsFBcMaO0fZ581EwhnUs1+Og12s+GAhVdLzuNV13kIuxt/YwM1DsVM1G7xxRAIY78ZS2hDNeMB34KtVGZlE6u1YnXriTVX0VihR7oEQispuv02Om4Ygcbctt481NfIoA7BZJfVU2FRXC1sk05yvDWkZtaybm4WvcfHYzhgo04HMWbjMW1EeHlR5HZz5KJFo1Omp7L0EM55+hWyM74lr+xexBGK66ameCZN/LE5+ldTfS0unZX9fh2xNHjhs39Z81kIwrpS3e1KWPU2V+5YwKt71lNdUcHwlAH0jU5F62VAGLVojFqETmCrtCKjvDEGmDB46dAcJ9bBkUgpEX8joXIqK4BI4CMhhBZlz+BrKeV8IUQN8LIQQgdYUVRCCCGigPeklJOAcBSV0eFnfS6lXORp9yngayHEtcAh4MIzNywVlT+RDiNg87vQYdQxWT69p8Pi77hOt5AN7q7cO7clz6jTYHMquugBRi+G7VHODPhoyjnr1r4EpXRvLpu/QPm+Cg9tPQlLKfmxSkMXTS7vTzu/efKxOxrZV5TJD9tyWJdjocamCABbybk8MOIivjr4EvcdOgCAX/YvNBJLjcsPn3szqNizhuLvVqP7YQHagQNw2TdS8sijRD391HFfwX+mdGXiy2ua712A/2VdyPx4N902l1OxuRwfTOzu3EiXNibJRpcLhKCmpqb1+7M5sBj1mKOjaSxY1Dz5ux1aNHoXGpcFt8NGyYavaSrN4ec9LoxGOxPNdky1ZhAtHlHpMoXU/hfz6eqdGGvdVHuCB6zO2kjoDkGgPNb9hcUlWVavbNbrjVq0BjvGkM0kDJmPVmfG4ZCUlYZSVjachob9JHbYiFdoPG9pL8FmSMSsNaARAq0ALQIhQCsEhfUFhNg2EkVBq+dlVmXilm66+6bQ90cHkXITJoOLV30u49zzZjC9b+wxffw9nFQASCkzgF5tpK8F+rSRXgRM8lwfANq0I5NSVgJjTrO/Kir/exwO5VhXeExWl0ETadK/gdf8m3nP8DwAA62vUkIwc28azORXFf8/HaP8oKyO8dd1I6lXCBpty4Zxel4NRUsKcPpq6R8b0Kr9tSUZFMhQHgy1t5pUDXozqfF9SI3vQ3F1BYOe3tic95+v7Px455P0zFCCrDN2NsFLHqXYfwABPoGE9J+K0yue6h8W4Nqg1Kv98UeMs+8h2KvtOAFdIv0I8zVSVm/jsoFxbMmtJjrEm6S7B7Dqpc2k1SuCbsOBDax/ZD0NGm+69h/O1RMGAGA2GsFux2BsvTpw6pT3YC0rw+hjgirQuMIZOWYZK1enYvSpRD4VRZRU2r8dL7bGhRBccRlajvJoKt34egdg1BtI0Fi4atZzbPxhFQvTV6AbG457qaXZKsbhZ0BfZ6feoGXIBQnYm5zYrS5yduyhvqAHwcH1OF0NlJUtwORVSVDQNMLCNxEYWEKNJp7N9hiwOxgcYMQtJQ43uHDjlrCvoYlGtx/GBhN9ZWsBUNxQjMGth8wYSqozqBCRBHjZqPLyocJy5j2YqieBVVR+Lz4R4BMOuW37wffqeynOQf9qvu+sUf7TB/u0KMInjUnAJ9BI5q9FrSb/6kY7P76/C4NDcu6tPTEeZUn0+bYKjC435/t1PW73th5Q4hAPjmyJEhZkO+I8wS+z0WvclDcYWfquYiIantoDi7HFbcPLUzXc9MtNNDoaj/ucty9Xvgc7hvqw6PbhhPoa8fU2MPmBIWhu7MbiDrU4PULK291A3oaFvPndcqx2J40e//ypo1uvosJMyuby23feyKqPPwbArS1l5epUALyaXNRpgqnURVI04gW0Gi2D9hayd8ePiJnL4datcMMaQMDqZyl9bjj1KytYl6ecO+0+XHHUl7FiOx9i5S23Mj59ndKfkIERpI2No/+UDgy9MJmw5BrcDjNx0XeTmPAwLpcOg6EHF110Ef5+5dhsXThvyGd408B44z6+69WRH3onM69PMj/36cSivp3YNKAjGreVIO84ZvWfxetjXmfu1LnMnTqXELsPP+59mW8CB/D8uf/H17Hn8WHQxeyVcZzdvbXr7DOBKgBUVH4vOgMEJUHN8cMo6s56DM76LwDZ7mg6hftwzZwteOm1zL1pMIOTQ+g2LIr8zGpqSlsm2aVbiggps+M1IoKU+IBWbZbXNLJEH8ToUhf21dtwu5yU7V5F3oZPKNzxE6VZyynfv4q5m9IBWFdsItZo5XKfnbz//vvUjX2+VXt7D1ST8csisjevZ/NPc/GxtdjeT7rkQbKqsnhl+yvHHWOvuED6xAfyxopsFq3d0Rz0BiAqIYhrZ06m9znX8rG1DyvsSTRIPaUZq3noyec4bNQZ0a1bqzbH3Pef5uumymP3DvoNW0nwQ/sJfjCL0Io9mNwW9Bo358bugbCuENIRIntA32sok/78J2Mk4w9t4Y4VnwJgDvJFALu1ZXyInU81TvJpiaQWNTqu1fNS+vVQ3uW8FWRmfYJW6yQ4aCBCCOyOYKSsRwhBb2Ml620RNDqbOJoQkzcaRwHldifXLL6Gcd+Oa84LLobtYS+hK2wgYm8xPSz7uag2lgk1a4j0bzvK2e9BFQAqKr+X+hI4tA66TjtxuYq9uHRmKvCnwmInp9zCG5f2pk+88jXaqX8EAAVZiurC7nSzZaliOx/WoXWQc6fLzX3rc2jSwcXlFWh3hrH7rcfYVXQD+xtnk1V5B7uKrmdz3g0sTB6GbWAoUie4ZWQSqclKbIIXfimg/q4CnJF9qXcYqHEoE8xPzz2BydsHp2fT8/3xGqamzeDCThfyeebnpJelH3eID5zdhaq6Rm6cX8DQWZ+zM1NxD5FX2cB5b/7KHV+lI9Hw/m3n8sSD99Jt6ASEoWViWzT5Ykr2H2q+D+qWyoD9hSSU19Db1AQ7x1Jz0JeGUi96xX6B3i9BKWgpQ7/7PaqcMWyovxQhAL1yFsGVtwtH+hpWuHqxJFLxFpoVrefXp6KpfDyZaIpp0tUBkIyGYNkyLRq8dDidLl69+jKev2gyH8xR9g225jqpq3sVAJttBZVVBzEaSzGZ0gC4MjqMevx4K+uXNt+T1lmCSxeKBG6a72JXly48sv4R+o6ZTFnHXN4ffxtJMdWIYImhcRslhjgMujM/XaveQFVUfi8GH9AawVZ34nLSjVMK7OiparBz25hkRqW0nH/0DTJhNOsozqkldUQMH/+cTXy5k92xBnZ5ORjndOHt0Yn3emct5Sl+nFXoYMyNIyj49icCd41Fapx4dQ1Frw/A5B3OqkN14KNB+huQg4Pp3DmC5H4dyczMBOD999/n2mt/IH3+dzTt/6a5L0vffQ1t1wSsBhcxVyomoHf2vZOVBSt5ceuLzJkwp01rl95xgVxf9i0bTF3YHpDGw19vJj6lnp/Si3B5VgT/N6YjHcMV1c6FYwdy4diBLPj4a+SXH5GYe4Btc75m0uN3A1D71myCG6wEN1jxDwsl6ra32361tQUIYEP9ZRy0DWCg72fYF8/BsP42tIAWGBIyFVnqYPp9OkAy2WJkSHkZ/UinzK8nQwP20KewF2bPuOrNOpx2Jz+88CH2xhoAvKuKKDZGs7xRRz8gIOBWeqXdQmbm1wgBfr5KIJ3J8QOJOriYpVV27myjv4NNQazQ+nHR8i5YNFZmTLqE+h998U9+iESTi5ulnrP4CV/TWIbEpvE+J/m39RtRVwAqKr8Xow8kDIXspccvY6uHnJW4ItKY2jOKm0YmceOIpFZFhEYQ3yeQfZtKeX/OjzQsV3wF/Rp1Cwsyl9PxJ8Xx2Zs7CyhP8Aabi7W7yymsdJBw2cW4dVb8mvqSPOJ2EgZfRUTPiWR5KSqL9+JD0egFl2YcYF99A7Nnz+b666+npqaG559/Hm+pOGTzPsLXkEurQe/SMyhK8Zzprffmqm5Xsa1sG6sLjn+Ws2vPVKJsxQBsa/Ll++2FuKTkyXNTyX3qbO4c1/mYOvExcSQdUKySYsYMa04ve//r5mvfydOO/36rlVWDXij7HC7AsP625uwG74uJuf0hzo5oUWvFoqh3TIYivvbbRL/eFYSYW1xZ5xT+yitXnsuhHT+h0SkWQs6IfD71tTEocSlSCtJ63opGYyAubiwul5ai4u+b6we6S6l0tj3FdrQr1lw1fkFocTOtZB4v2Z5nR34OKeuGEXe/hC2+vG6XzDauo5Q/5uSyKgBUVM4EHcdAZTbUHmsJhKUMvrwE6gowj72PV2b04t4JKXgZtMcUjR5uwKGxY93gi86q/Pd0Czd+ltcwbiqn46odPFJRASYt50gjWgkvfKt43dQ4TZg0LTrrrKxyIjzqjChh4tW4YBo1OqbuzufF9D1ERbX41d++dxe3dFrPjZf1YszVLT6HNnStooN/h+b7GJNi8ZR38008+EDbgdfz9+wksTGXIZUtvoS8nPXE+LraLA/QcUhL1DT9TVex6mvFWtwnVXEXYQjS4ntZW9/SCuLbKwHob32P7zv/iwaNwOGOw375Tlz/dxDve5SVwyu3XkiQZgBNYU/zTMqjfBs6nKsjg3BpXMw7OJ8BhpaN8qKGDAASkvpw1oNPAFDrUCbicO8yhJC899rlvHL/PXz90y1otS58fHL5dNEN3PfxnewWPTgkElmfVcrR3Dp0DEa7le3dFCuoQEcN+YVmVrh6stR3MI1aIw0mPdFODTGiiTFJvse0cSZQBYCKypkgwfPVuvxxcB4RE7c6F94dDfmbYOprkDiszeqHiQ2P4uue/yV/wip6XONP6jWRBPn3QWiceKc8RYh1HaLKij69kqnRwZwf5Mf84hp+XX6AeiQ2reDQ3kruf2UdE+Zs4pVl+9Htr+OxA3tICdOwsGcCMdYGnq62M2XZBjr3UibeWFGCw63h5deWULvhK/RDk/lpSBF7450MfWYDE9+ay3+ffZ8PvvyAsPo4+mVLUjzeTI8mMDIaAfSu28GbsXs5t+gH7t7zCvLqs8kqa0NAAiajgZwZNzbfN77+KrK2lJr1isVUxL23H/edyb0Lm6939pCUBgk0jmGU2t+gfp0FbXDLqkar1XBH0gNYTFFYNX4s73UtMb7KXkGpS4O5RomU1mS10GP3LpASCgtY//0KEn160M2VxhuYCapXAv74hlVSovfD5VXT/Ayz3ER/c4vz43OLi7ll0S4arC2rj3A/X7o21pAflYgExu88wNiMHIK/rYQmN1++dD49b72cvvsXUZZXyWUjWm+OnylUAaCiciaI7AEj7oUdn8Ocs8FmgQOr4K3hYK2FqxdCr0tP2oyP3geruZ6wWH+G9e/DiP5dWDL1bS7qfBEaUUtDyRukuv9DV3M2d3ydzlkjEtEDly7JZCL1jCgsZviHG/iqqJohZhMNgC6nng21BkZur+TTok2sGDeQCxx1bBNG7jeFUePljT/1/JDfDafUsiWzhhE9J1Ll72B44ShsVg37LD44assZcGgK5+26i+tn9sV19xdtjuGcu+5vvt6zejl2rZHMoHh+Tkniird3Hnfskx76PzL7KxYxCaUHyLz88uY8r9LvlPd5NG4X7FViKq/qF8y/S58keu+dPKftQ4Y2j6bdlRT/dxNFj66naY8SR3lEaiRDK2vwqvuZiRoDo9ZO57GvJ3Px2haz3E3lmVREjCda34FeYRdzVl0K/UMnMkgXQA90DMwZSkLYq0wc9wk3zryepzffxr9W/JeOqVs4d8I2QpIfY8OSer5bbeF6h4G5RidJ63ezo6xFlz/UW0+9bwAHU/qh9RhM6aWbTxc/QUFpJn3C+lOlDyDbO4luUa2NAM4UqgBQUTlTjLofLvgACrfAU3Hw8VQwB8HMlRDd+6TVAYQQdPDvwNrCtc1pRq2RBwc+yMZLNvLwoIcpaSymyPQ6On09V32/g8NKi+5oGRvsy91pMSy+ZgCfPjSaCO8aogPLea6nG5Ow8WONN15GIy+N6MNgRxFNBhONUUH0mfUzTbLFzPKX118BCSEOLy42b2dG/QbcOisOg+KDZ8iBKwgIDGlzDGb/AK587vVWad/GXMZXkTdR1uDi681trxw0Gg2drrui5V3saynXuHOv8j5drYPUOBbeS1HGZwD4FhsotQVhRVGtbdLvx43EVWvD3ejEWaGYZAb6mbg9zoVPzRccLD2I29qLwthxxOifBWBX9Vq+viCcvPhxJISOw6A1YeliQ3dlAp2eHIZdZyNAhNOh20QCQyKIi44gPtRCg8Ob7btyEEKQlBTOKukgrklydadILjcpewhn7T5ARn4lhUWZ3DggDZPdytbklrOyy0e+TnqPWzg7YzCTn/qaeR3OJtsnmWCfY01gzwSqAFBROZOkng8Xf67E5J30HNy4VnEWdxoEmYLIrs5mce7iVulmvZkLOl3ARZ0vUhL0rf0nfvfEeN67Zzi3XtyTjp1CyCzKo6QhgOmpei5NGsTlAaVUEMi7Wcv4aPsq1hpjGNO0jxcmTmD9MzdSb28dr3f4bsWzqcntROdx0iM9m5EFWjchJ5iUQmLjueRx5ZzBKI0vz0SXcdNI5T38e24GCbMWtDoncJjkAT1x09q6yC0EmqE3IoGFj13I7NmzKdm1GvLWsXXnx0yIjeYjP1+2FIzkMuM2zo9s+cp20BLb2GdwFC63i6qqcmbtehCAtVk7ke4GHA2LqHc5+OHQN5i1XzLD8TH9+/+LGJNiouuTKTAtXYTAjcGpjPvgd8rBv4VZ2WQmKGP7JbeGOTsLmfTOej5DObm7c2sRzw5K5seoKEJtkl8X34f3ayNpeLwXVoOJCv9Aqj3+kYIrduLrzMGp1TFKd4h7usw57js+E6hmoCoqZ5rOE5Xfb+SwY7O3drzFqvxV9Ivox9SkqWg1ypdtdrXiF0jvagk8//LFaei1rb/nlu7cCWgY260jALNSJ7Bi/XIeKo7BKO0g4DlfyZd3XkOtTU/XDn7sOdAyeSYe0mJp8dqMj0Yw0vgt//aaQGBiJwYnte0WAmDR1pu5o/Z8Lh9+IQPzO0AhnH9VB6K94aEF2Ug0ZBbX0/Uo1YbRqKfu2TcIuOcmAHYGdyDp9VcwJwewMHMtG0kDYO0Pc7jAOZdkjYYpDh35lvMx+8Uz+447yM7O5vPPPwegZpQXfc5SrJg+n/8+/618SXmQFhIrezLi4HR6eWuJC5pCtaOeyMC5hJgsNOUcQuuWNGrn4vROBa0ZS1EK2g8/wnzFWBo/ziO7vIFEKflq3wGE2x+A+ftszN+XDsAl3t7QALGekJ8DOoexwNfI+oONvJ+jnEe45Pt3qPfxY31yDBJI7rOMkiIrjopgUhu2s9p7DDcMb9mEP9OoKwAVlf8xLkm5BID9NfuZd2Ae/1n3H+rsysT8xIYn2Fa2jU6BKdQ2KP995906lHPSFGsZl9PNB19mMPiBn3lxpYZon0q6xvcFwKQ3c0tMAAA2YWDc/nl8/ton1Nr0mJOCmfDEp1z9YoudvdOvdfjJGHs052kvZ78I5d4JKSf0eplfu59qEcympBYHa7Vvf85lywbwhUGxqJHHiRk5aMpIqv79CHM7juDxETPp1aMDeAeTF34Wvl4GOprr2eWMw4aeoDv3sVo7kJUxQUyfPp3S0lJcLhf/eeAhvDGya6diIeW02Vsmfw/RtclogTij8h4D9b78WnENpfYEhJQYHBJt4n5C7r+O4LtnYAoopC47juIDisnpu4HBdP9pBSv1vnTT1zNlZAJoBT16htPN18glDVpKfbX07tHiwiEuyp8JxS17DdGlhxhAFOaOyrvYv9VNdZUPHQ6tptuOveRJf6b1agkYdKZRVwAqKv9jDI8ZzvbLt/N6+uu8t1MJs7GzYifri9bjpVdOze6rzmJkFwMrM+3EBCppe3aX8e+v0tllb9GT39rHiuLIVyG2eDVecgQDWcfw3KXUeaK61uWWU1xVgL9vIFJoENKNNSoBgMDAQiIi9tNr+wO8hpb8MBN9E1oLh6O5bNAHxKwfi68uDHgCncglyKLY5Q/UZNJZHGLTwSq6RSlfzk6Xi4q6ciICI6husPNYXQRZqVO4elACGo3AbrdTU1tH585d6BamZ//SLfyXW7kov4IazXps3sN5772WkCQdOnSgU3AiOyr20mRp5I4vb1aC2QI7r9yJlJLnX/4Mv4rWQqzUeogN1cPp472AGCppMqTh43YjtBoCLh9B6au78V8rcQF9feqpcDjBbuXGzomcn9KRV87qSl2djaLntgASJsa3al+63DgdPwOKUDb4TMfkn0tivyys/YxUlkdzoKAPhSMz0CU4sC230jn8jzEBBVUAqKj8T6LT6Lit922Mjh3NJT9fwi3LbgFgxxU7SPBL4OF1D5PVuAgYTa/HljKraxQv7CnCjODJ/gl0N23i59p59DGObG5z3ntXYe6whrf4CC1OykcHMvjWZ8g/lM/uFz7im3tvwGnRIgCn2ZdkVzQ9XfFkhD1PSOghTH4G0uoE4yZEKZZNJv/j9t/LK4YenR9GHPLDDbhkaKv8gzKSR+btITbQzNiu4fz8Sxre+kZ2A59lXkBWyXBuGhbPuEgb33zzDXl5edhsNnr06MHOnYolUWpCKHPnzmWsbgYODUCL3x1fX1+6J3Vm+9Isnn7uGbbGKg7xRkSPAJTNdu8gA4Yj5v99tVvok7ENs93JnIsv4Ur9XmIPvErhqyVE3/YeuuhITL0X0LQtgaKO1cw6+0JmHTXuilorBS9vI9ghKZmeRN/eLWctKub8hOvH2yiNa1F7CV04h2z7Kd56NjGxu2lwhmMwNhGfoJxBuGVq/CnFIfitqAJAReV/mO6h3Vvdv7b9NYoaigDQBWyCvNEAPLWniCE+XrxwfX/Cw33I/yaXvmEZ1NUpk42UEnMHxV+/Dif1hWZ8Ym+jW+RANlbsIbRrHeV7lLI9AoqxTZtJ2iplA7Rzxm3oLo4h9gY/XHOuQff1dqUzU16BPlcet+/R0TOQEW4Kf/wViTeWTu/iMyiChZZk7J9tA+CGj7YwNnwL09NaHOBd2uVbLM4wemg0/PST4o66W7du9O6tWFKlp6eTmJjIkPHj2fX22/SJjycnR/E5dO+992IymRBC4Ha7wXM4u1t1N24+/0b6Jw5sfs41F5/DM3s/Y6BLcd/Q1FROpF3ZNJ5s6UbAfXdSOOd2IqvmYi2djSk8Bp9zZuC7J5Ei42TaCmGybGM+Q5vc5E6NY+gRkz9AzZyXSRpehakphGivWrS6MA76FmJ3a7A3BLE3Szkj0qXrSgBMphjG9J923Pd7JlD3AFRU/scJNrVstr67810mJU4CoNZeAx49+kPdovnk/pGEe3zseDUm4F8wglq/NezLfpzy4u3NbezdMpbcX6IJC1B000vzluIwdKDLxfsJ6WBgUfBY3Gs0PNbNiFNAuPQnef33iK8uRVff0g7z/gX7WlsqHY3Qagiaobh+qMmIpPgbL7p/tp/0ixQVyIwGHRekfgmAy/+R5no39vyAtDSlf3q9nmnTppGUlITFosRM7t69O2+/rexXTJkyhdmzZzN79my8vLya9yY0Gg133qWcHrZpbcSEtpyS3lyymQsWXMDXnV7kitj/srehkJDsjJZ3ro3G6O2FbtgtaIQbywYlYKHR6M2BsJ70yfmequqSVmN1ut3Yd1VQoxcMOiJqW2ZKFzJTuqCx5iE0IMK7cnFCBn4+PXH7H+tB9mBOP2pqwkjp/NgJ3+2ZQF0BqKj8j1NprWy+TgtNa1YHATzUq5K07QmE765vth4CsGZWEeA7ktrYleTnf0he3a8IbziwOJbQ5E0EXVnHrvKfGcBYLu96OZsKHiRQBlLUXYO+XMvigDB+jDHwY4yBn1daMB4KwGzY09y+BMVY0z/mpP039wyjfm0Rjvx6XFXKqYV5XykqmeLILDQ6O44mLUkBJSSPzsFmK2fDxnEUFD7G+PF3sWTJEvbu3UtqaipeXsp+h8lkam6/urqarKwsBg5s+bqXUrJx40by8/Lw3reDVK2Di+ZMJjo+mU/P/oxrF1+LRGIQkge67IUuDxAzfRYlL23BvHs1xWIrjYWplDT6cMDVhZLd27E2fUp1g5Xyyj40OHqy6q0V1OnDcNhcOO1uhN2FRsIjeJHqsciqmd9yeM0nShm7oTELl9SRb+tB154fsW1byzkAi6karIE0FvYmIKD/Sd/t70UVACoq/+N8O+VbluQt4d2Md0kvTwcp0brBpRXsD9jNWShmgtLpRnhcBoff1Yfil1oiSAmffbhru1KfL4kfrbhjSND+iMP5X9IC01hbPp6MEhO9g4sZN2k6H3/xJXPjLka4alnpu5FLKovhsu8gqhcbv32VlQcauXd0KISfmouC0GtTKZq9vvn+IY++fnl9B4ZtjMFrX1+8wgeQ656DMSYAp7Oeurp0RgzvxcqVK/n222+pqqoiNlb5sj4s7BITE/noo48A6NGjB2azmdXLFrF+zUqaMKGvLMHkcmBywbQ1UdRureHzDp8jkQyPHs5wHys0rgRgt/UZ3ht4OTsSn8SGAd5d39xbHEC6cmcgBF/RRJ3DFy+jBv9AA94mHb5CkL63ku8MLrrvKWPB2ly2b8lkRkQqZ7/3ONr3B2Oz+mIylYEAm/RDb9gL9ERjsOG2G6n0PURvVyL+oVvQaluE3B/FqQSFNwGrAaOn/LdSyoeFEGnAW4AJcAI3Syk3HVU3FvgYiADcwDtSypc9ebOB64FyT/H7pZQ/n4Exqaj8o+gc1JnOQZ0ZEzeGi+ZN5+unFKdqH4/WsKDfz9zV+Qqse6spfOhXYv47DGeVldLntyK1LR4knVYtAfppaHRz0Zla0jcdeJ+9uSYMLhNaXGyrjMT6/H+Jyq4hruMwoorf4sPQXBb761mc+Axo9dQc2EITfWDIbcf09XhoTDr8JyZQuzAXgJgALwpqmkiJDKDHwGfJaXiGitBfqCtZhKOuxY+OVmuiQ4cOZGVlsXHjRpYvXw7QrPNPTEzk4MGDADzzzDMAzOQz+stqXqi+AH1VKcJootbPhl+5xL9Rz4sbngMdzOp3F9s2n0UJgUzr+TnLt53PgNitdLImYc0rILBnL6ITQomt/YWITikExaQQEBiCl9fxD8AlzFrANrudcz7erCQExWJPPYs+7/8f8UYnTTo/jM56Mhom4Re3kQpCEG4tTruR1K5dSbJtIiTlVaIjzjnld/t7OJUVgA0YLaW0CCH0wFohxELgUeARKeVCIcQk4Blg5FF1ncBdUsptQghfYKsQYqmU8vBa8kUp5XNnZigqKv9sugZ3Zevl28h5SlEZXLHcTZghFFs3j5dNCW6rk5JnlMlH42qZqA4sjKWp4nu6XprTnFbv0lCS/zF6raJSskUE0KmhkkzZmeSSlYSX3E9OiCIsigwOrC92w3TrZs5iDWexGrSzT6v/ps5BzQLg8/N7YvHW0zXKjz177iFo9HYqUfYXOiTezoGDLwGKtU6/fv3IysqioaGhua3duxUV0uHJ/0i20ANtfTXG0gIkgrPvmEXnvgP4/NsXKP5mOWarljofJ9v3v4KPxk1o3K14h3bCqh1EUsAGht0+jWDfIwPEH+u++ngEI6hEckNSGL56LRt3Z7DGJ5KXRG9eZCO+zkIOBI4kP+lW/I2zyf9hNiFAdkoGmZmZDB22F4DY2KtO+Zm/h5NuAksFi+dW7/lJz++wPZM/UNRG3WIp5TbPdT2QCfxxpxpUVP7hGLQGumRlYvbou/sOvp0+HSUX+9pYh4OyN3fgPUCJLIZo+dLXmZxE9i/D4KNYuXRKfgin7xAiRDk6r6006CzoSuo5UG9ACg370kZT7aPYn/s4NXxSVILJUgofT4PE4RDd57T7ro9omVR3zVnIirkfU15eTpO1tYfQw5N/cscHAEhKSmLcuJawiT4+PkyZMgU4SgBIiVduFtmZNrIKzbj1Ru747Ds691VcLgcEK8F3Lo45j1/OX4y7eiElLh8GdlD8D5l9B+FraKDUc9L6t/DzncNZeu1A7ru+H7de1Zvrx6YA8JN7MJ86x1BYEUbcjV8yfFoP9uYmoXGsx6lt5Ny0Xmg0LSo7X98/xvvn0ZySFZAQQiuESAfKgKVSyo3A7cCzQoh84DngvpO0kQD0AjYekXyrECJDCPGBECLwOPVmCiG2CCG2lJeXt1VERaXdEffhByQtWUzIpIkgJQX1Nv5NE87SRho2KtYp+X2ebS6fdHYB4b0q0Wq96d37S2Jjr+LsPq9Q6vImzDqPCYO/x9mUTbEuoLlORFME3d3d+bHIj1hHEgR3hKJtSnyDkl3gtB3drZMSerOyetmk3095ZQWvv/46et0sUlNfPaZsSMiY5utBgwY1X1ssFhITE1sXlm5MhQfQNVmo1/rgFZPEDS+9hVbX4t+oslbxnTSq41jSc97EV+MkPOaa5v2EAF9lQ3tf/np+K+FhPiQnt1htfXFQcZ7nQkumfgrBj23ii4wcnn7mZbLMWbwx/Evyw9azcvlqeia3WFgdeXjvj+SUBICU0iWlTANigP5CiFTgJuAOKWUscAfw/vHqCyF8gLnA7VLKw85G3gSSgDSgGHi+rbpSyneklH2llH1DQ0PbKqKi0u4QQmCIi6OLjxevVx9giO4gFxu3tyrTFKSoE6oPtJwk7ZB4O36+ytkCL70fU4cub84zNThw6JMJqOjFHsMqDpqzuMN8Cb7OPGzeHeHqRYCAyv3gskFR+mn32xjnR+DFrVUqer2e8LBJDB+2HUdjIIdW3YHbqaempmVLcc+eFgukxMTEVlZAAL770tHXV7Pbpwtz4i7nGf14zn5sLuPv+5iy8kosjXXk7FLOHhzM3Y2lfC6lThOjOt/a3MaglDGUNSXgrPsQp+v4wWtOlRd/3MDCXCcDw9ykPzSWJx6+mvsXrOWhuXkc0gcSaVf+XsxOFyG2SPJX3wFASucnfvezT5XTsgKSUtYIIVYCE4ArgcO7QN8A77VVx7NvMBf4TEr53RFtlR5R5l1g/mn1XEVFBYDzzz+f7t330fjVQTjiozzcOJu9Oz6m94jbcOi3YKnPInv/E2TvfwKjMZLoqIuJCJnSXH7D4MvJ9A/m1s/vYri2CYd0EaC5A68ptTRYcsAnlDJNT+pqDTgWFBBifZvQpwacdn+908IY1zCO5cuX07dvXzp3VgSCXu+Ho+heGkuDqd4/mkzdLKKilMNWUVFRjBw5kpiYGDp0UKyezjrrLBYv9pxDcCvqrsioEsViB8j3igUJ/Z/fgM7t4IoCJVCPxbqEZLuF4sQrKdhwE9l1S0knldV1pRxs8lgn/fRv3ji3zW/SU2LptmxeXV9OopeDObdMxmRU/P9ck2XkemFifnIchl2dOb+6J2h1ELaLqC5LkRKCg4f/5ueeLqItl6ytCggRCjg8k78XsAR4GngWuElKuVIIMQZ4RkrZ56i6AvgIqJJS3n5UXqSUsthzfQcwQEp58Yn60rdvX7lly5bTGqCKSnvBVW+n7LV09gyYAcDoUftbOWyT0kVJ6Txs1mKqazZSVbWmOc9Q4UVw73lMzCnjleceweDvR6hFyzujtrA+wsCv+2vZqHmQfaUdic9bTNLBnwDokpV5Rsdga3SwbvGHuPyeRqs1MmrknhOWX758Od27dycwIIDNG2fw9Ma+pJcrK5wJsRoW5beOpRvkymeAvojp2pW4RxVS4RS8Xmak2qXBTyuoc7XMh1+ctYDUiDhOl5y8Ys55fR0areDnO0YRE9ai3V5x/9fYhIMRD57HV199RW5uLgkJEcTGtajrxozOaavZ34UQYquUsu/R6aeiAooEVgghMoDNKHsA81FMOJ8XQuwAngRmeh4UJYQ4bM45BLgcGC2ESPf8JnnynhFC7PS0OwpFjaSiovIb0foaiLyv5fCQzROY/TBCaImMmEZCwk30SpvDwIHLAGisNdOx9mq6dkykT2UpmYlJhO3fR+8f3qfWpcPHoeeT8jnsK+1I14Qi/Opym9ts2LCRM4XF6eK8zIPMCOhPSfIS+vebd8LytQ4n1+lC6L6rkJ2579Bo306cX0sQmUX5bsZ3LCbn8bO4e4qR2KBM6s1eLHQP4CHn1ZQ7BI8Xe1Ht0vBEt0tYe+kONl6ykbu6Pw7A3b88c9pjsNoc3PLSEhxuDU+seJ3C667EVlMDwJo1a1il30NNdy1eXl706dMHrdRQXN2Va7a+RYUMJi722tN+5u/hVKyAMqSUvaSUPaSUqVLKRz3pa6WUfaSUPaWUA6SUWz3pRVLKSUeUEZ66aZ7fz568y6WU3T15Uw+vBlRUVH4fQ4b8Svfub2AyRZ2wnLc5gVFDM0lbNIQ7asxErEhnXWQcTUYTOqcDh0bDh1O+4v13WvThEcPHoO+kqGw0Pj6UPfMM0u0+3iNOmTqni3cKytlap/gEujunnkOy7f67amr49atvGbYpi0aXmwBZRVX+C0ih59krZjMwqiXs5JL9kdz5+ZfcOmQsa/59N3sfVsJMFsgwcg5eSqzni3/xnp+QbhdmvZnxUYrPoULXKlZtSz/lMVSVVND9oZ/JMoZwtymP8J4d8cneT8bkKez56CP2fPIJfrVWzp50tlI+OoGM4efwVXkDotLOkuIxBAWdOGb0mUb1BaSi8g/DZIwgLPSsUyqrMRiIfuVlLBERzWlBdbXUe/tiNBgwd0/Fu/+o5rzMdUWEjhsKgEgbiHXPHuqXLP3dfV5cUcszB1v71rloRw7uo1TUDRs2sG/gIIIefoh7nn2Ezt4mavGniCgqCUen8+PLf80i96mzWXGHYkr5455A8soU006NRkPnYEVg1RkHsuDKHfxfYC9WSwtDP+7FdZ+cx5SF56CTWqZWjaRmYe0p9d/tlrz59jwcGh2XGcq5/vHb6f/223DfLLRWK+K/TxGb543ROp66YkXIfVhYzhqNZJrTzdQGA9P8fiEgoN/veo+niyoAVFTaORqjkUemjgfgSp2LUVvXY/PzeBG12zGGBdF5rxIAPjDCm8bgRFwaPXVWA7rISKo++aTN8I6nQ6SxdThKAZTZncwpbDkV7LbbOXTV1c33fbN28YC1Gim0uPzHESIL2Jr/Y3P+Zs9p4RCvGhYXabjw221YHS5enKA4mesb7ERotVw/eQ7/9R9EvQY2urMZrR/Cj2O/Y6r1EvrUayg6UHXCvi/6eQMd7v+Zd5vC6F22l0fvbjnF22XqRLq8+zwN/foRVp5ObP5yfEOUd3tlZT6PLl/KZE0F48bPwuxT9ae4fzgS1ReQiooK3XwUJ2tXXX8ZAFH5eeRedDFNO3YAUNrzXwDsWauc9xyYmIZ+51qC7vkXZY8/Tu3cuQRccMFvfv4F6a03Pg+Lk2WV9VwTo5h/O5325nxNQgLVVZD1/hLE1AnESsUfUHldNjW1W3E4atiQcxCI5JubRjLqhXQQTrrnfId32HLMiWY0XAOA62AWE9OX0VVfg6Pj/9H5otkA1LsUQ0XZdHyT0PU713Nr+i+AojY6++A69vZ/FwCtl0TotDjr3URfMI2azZvp082OX7DyrqXQMNRtw48OOCrMhKce37X2H4W6AlBRUcGk1TDQ37tV2uHJH0BqWn8rOgeehcFWR22TAWPXLpT+9ykcZa2D1J8OXc0GIm1lcNRK4p3UlohaNp1kSS/Fqsmdm8vW3vcQ4zect9ZW81GTsjIwVXzA1q3TyciYibdb8cQ56oV0JS/qK0wRP+LS1KM1lbLFMg/7ltXwyXmAg5jxXzdP/vu2F+NvcdKAJKprSJt93pe+lxs23IlX9NfNab3L9uEdpdihupoEznpF3VTz7Q8AbDNC8X7lfMbQIUN4PriJl7y/pymmkAD/0z9d/XtRBYCKigoA/f29ufaBp9rMMze26OcTugeTNEWx/7c9/x9sezJxNzRQ5fHK+VtYvnAQP796HStuvoSIihZB4q1tORG7s2In701Q7st8zZgqfiLBoKGP0RvvumMPidpjfHAm+SK9XBhjv0Xvt7NV/nJbBgcXn4eOYtyT52Dop5w8rq+30fD1PgCsF3ZsM/bxvnXbGP/lfupyHuTfhoe4a+8PjMvbRPxV44lbvp+UHdvo8PlbxD58A2Ej/NEa3LgDDOzbt4eN338DgEGn4ZK0L5k45EekVoufX89jnvNHo6qAVFRUALgoMohXYuJpMHnhawpDF5KCfb9y0Cp0UA+Kc5VyhVvzKP//9u48PMrqXuD49zczmcnMJCEhJCELJAECYQkghGBZDFAURa2lFllat1q9XlvXat1ar2BbudflumC1rRastVirrVoBuYiCyCIEFAQDshjIHhISQpZJMjPn/vEOhDATSAJkDDmf58mTeZfzzsnvyby/9z3nnXOevx+vWDAp9/Hy1cuWE3vvvaecLP5U3PXGyd1lNQaxm9MrAuV2IxbjNLWz3BgAbssAodQZD+wldOltqP7RZA7s43e8TaaJuAdEQN8KrCU5KIS3rvqA9MgESit2M/e9q5md2JuldWNIyJxyvNy2tQdJUSZKpyYxerT/k0iLnlnCvBKjHf87NQe4ds5t5L36FFdUfErPJcb3IsRmxzYqG9uobMIiCohO+gv8YheR+aUkpBlPUZ3YbxIeNhiLJaxDcTsT+g5A0zQA+juMDsgfLvg9zkkPYxt2NaYexhehbHXNk9IMzV0EgGf6j1uU95SXQ1MTHTGr4dccneDgmgctpHmfpHDMAG6ZeTm7hmXgdRkTqRx2GZ2xq+8YxyfDS+hTUY3gpfi7Hl6NnclDPEFZ6Z3s3ZNF+aG+mCqNhGJyVwJw97iFpEcaJ/S46EE8NfrXuEX490lNX44oIw7164twu1s+4vrxBxuZVxJBWGMdV+9ZzYs3G49tNlXUYIuLwI/XA5v/BDGDwNGTxEGDEd/YQx5P8zSYPSJHdShuZ0onAE3Tjls/djD/N3EkpulG27tz8q+wxI+k39Rhx/fpWbkLgJCli1uU7XnTTxCrtUPv+5kazPRG44tXB6q+ofCO5rkGGg8Y0yZeNcB4uuaz4s/Yn9RA0l8WMHhXLnU3LOenF0zg38MmsHv3YYqLB5Gbm83E7bnM3rQSYyoS6OdwcrAoh482PInyehnom1qzwdby7iEjM4Gd4SZS6rx8tLi5H2Th//6dG1cbifAecx4/3fk+Jddfh6eqCneNh5D43vjZ/nffH1Hjt+lIdfPYTT0igpMAdBOQpmnH9XP45hC4qC8Fy4wTr33sbURPHEPY8vXUlR9ttWzMz37W6rbTGdknki/yq5ib+CwzinZRu94YHVSsVqxJxiid6T3TiXPEUVpXyviE8YxLGAfApJ7GlfeCBc39F+np6UydOhW73c7GFS9SC9zx4Q3Ht7/pVew+ahx3SNrwFnUJCTFz8YPfoeihdQzZW0NlVT3rV3zKUyUORjQUU35BDH9wjuRi69XUvP02eyZOBCWE9E1pPojXC7WH4J3/NJYT/UZh4Isvmp/66ezn/4/RCUDTtIB63ZxB+Z+MjlPPYRcZk5LYufjDVvf3HDmCpVfgJ2ZO57nZF7C79CgXD4mj8L4PODZkMBYL4nAc3+++Mffx/OfPc9fou1qUX7x4MS5fUxHA7NnGsGJlZWU8FhKOraSMzyIT+NrUxEar8OSu19hkUSR5opiccYlffUwmE5usiqxGYdOCjdyuXIS5XYSmh7Kvr3F3lDfjh/RraqT6PWPICqkrbT7A/Obxf2pNoTgPrAfAVVPDqw/czsCLLgBfqJzONEJD4zsStjOmE4CmaQHZ+vXAPtw4S5l7hjJ8chJ1lRPg88CT+NVu2ECPK68MuO10+kY76Bvt4OhHH1P97+YxgFRdHa4dO7FnGE1Q01KmMS2l5becDxw4QF5ent8x161bh2lpGU6cjLO6SHYkUTJ5Lhu3/I5NFkVCk5trVCRmk//Y+16vl5hx8RzaUIo31IrD7GVIDzdrBg7jxsNFLOqZwMyjEHnRlfRLG8ED2/5An2N9CQeN8ZEKXO/zkywH26PMbO1TRwJQ21BDfXkVcRuTqEqKoX7kIRISZnUoZmeD7gPQNC0gESF67mCi5w5GRLBYzUyYPYiBOZtb7Jf4/HMAqMaOdQCfyOR0+q2rXffpKcusXr26xfLtt9+O1+vl05Vr6OPtRbLXN0GLtzdZQ2bx9KAbeC31J6woKGL2kZ5+x/N4vFy5fDszbbVcNimMGy90Uj4mmc19krmxJI/HZlzKY1VFDC0pQICtA4ayb3gqJkcE5G9GvXIJpcVONlh2IzXGF9xCexvP+EdH9yYRRYQ1hsjibABiYy9rZ5TOHp0ANE1rF3NYGMmv//X4siPTaN/21vp3dLaXc2wWsfff32Kdp7Ky1f2bmppaTAuZmJjI1q1bmT9/Pn3cx2bmMk5zCVWbaXzlHrIroxiWbHQom6s/p+BX62iqbuC51Xu4f8VOiirq2OJo+T5zDxfz9bSxPD7n+1hMJm6eMZ1Vc67gzTSj6Saq6Qim5JHwylRKt0ZQvqYHZeEf88ukB1lYcj9L7l0HwLOPvETJkAwOJD5O8aX/IDw8g1BbgM7jTqKbgDRNazfH6NE4L5pI7SdrqVltfOO2Mb/grBw7+sYbMNntlDz6KADO8eNb3ffk7xwUFhZSWbWD0FAzdbVGAlA4qHFfgcP8IZaC15BCN3wGSpmpUneB28uWjfn8LqQWrPDqzr1+7/P01YGv0itrjInqo9xHCUkfBevBGu7BBIxZvZ3c9Hjkm2xSMqI5uKWYnc6tXLm2CHO5F/MjUfRLvaP9ATqLdALQNK1Delz5PWo+WcvyJQUMCo2G116j98MPnZVjR82ehWNsFiiFzTcDWCAWi/8pbNSopVgaw+i35lkAes4ZhC31z4jTCh4PruWvYtn2DJ6U7+NuyoQ9VexqaoQQmN5gpodXSA2zsbDmENV2B4sOtz738Tdl5RAaTWTDEXD0gskP4zI/zidXZPH49pthA3x26wRi4qz88+57kMyD1EbWEuesxWM9SnT0pDOO1ZnQCUDTtA6JmHYJXz/6NId7DqEgMZu0ff88faF2sKWmtmmU0csvv5ylS5cyZ84crK5w1q86SnbZ9wGwD4vGMSK2eWezhdCrboKrbkLcHry/Wk+dGR60N9DHBc9OSifcboxMegdpp3zf+poanq/10s9zkNTDtZh7xbO+oYkNO8bTWFGFNWkVtrqLiE2OoHH/fl7M2Eh8eAOeH0ZwxH6EwenzEAluK7xOAJqmdYhYrYxa+z59Vqzl8IP/AsDrcmEKPfMhjb1eL4sWLSI/P58ZM2YwYkTr4+SMyLiAkCNx5K/fy7YPngaxQYqxrX5HBbue+5yBPxuBydzyZLt372GcwAtpNkK9sHbyMEJDQ/yO35qn3l1BQUJ/lnzxC0ISLwCl6LFxIWKdQ7xHCO21go0/+g0iwt7YeFTZMAZFHiEkrIjihlimxF/dgcicXboTWNO0DjM7HPSeMY1et94CgIS0/QTamoqKCp555hny843pHdeuXYv3FLOO/emuT1j31l52bTAmdBdTOO9WNT+RFFZUw/4lu/3KRcc4cQtMViFsGz+0XSf//Xv28XJMEpcWfsLkIznQ50J45WJqrB42jDjM6wP3okRwhDjwKMUjK99gcl834+J3srV8KKPTLsZstrf5/c6V0yYAEQkVkU0isk1EdorIPN/6kSKy0TfPb46IZLVS/lIR2S0ie0XkgRPW9xSRlSKyx/c7KlB5TdO+/WLuvJP0r3YiZv9n6tsrNzeX6upqoqONTtzy8nLmz5/PwoULT1lOTHZCo+7BFnEdAAm/HY93WgoAjYddfvv3inaQm+okc3cNW5/bQl1T6+P+n+yJzz8BE/wm/3kaXTZCL7sFCjYz1tXAY+pjlt76Bl9eb3yJzuX2sC5qFLtco9j8cSZZcVtJ6dv5Y/8H0pY7gAZgilJqBDASuFRELgT+B5inlBoJPOJbbkFEzMALwGXAEGCOiAzxbX4AWKWUSgNW+ZY1TeuCROT4IGdnqqDAeJqooqKixfry8nIaGxsDFQnIZDYRnWEkEbM9cGv3d2YOBmBApZsPln19yuMppShpaOLDokLe65XBtcXvkSBeQqKjkOcyjJ3SppF+bx7WkOare2eIheLs4bw4Jovrh05gUtZLOBypbf47zqXT9gEooxfm2AO+Ib4f5fs5NvxdD6AoQPEsYK9Saj+AiLwBXAV85fs9ybffq8Bq4H7/Q2ia1p3U19e3us3jaXmVXllZyaG4T4kpNeYpTraXcqA+jv773gGm4Ko0nuAx7Q88t29klB3PfaOof2IrWRvKKZ7sot4i5NU3kFffwIH6RvJcDeTVN3KwvoF6r9EpHeGp5+f5f8Nkd0JUCkQlG78HXQ4BhsMWk4mIrGwisrLbHY9zqU2dwL4r+S3AAOAFpdRnInIXsEJEnsS4kxgXoGgikH/CcgEw1vc6TilVDKCUKhaR2JML+977FuAWgL59+7alupqmdWFDauJxuj148LLb0nxdOXzYSOz2lu3mO3fuBGnuHzhQHwdAWWo2Xq+XsN5O6gG7UjRUurBF+XdQR0c7+TjMRFqNl9E5uXhPOIHbTUKy3Uaq3cqknuGk2G2khloZKkeJmbwbLB0b/fTbok0JQCnlAUaKSCTwLxEZhnFSvlsp9baIXAO8Akw9qWigmSHaNXu0UuqPwB8BMjMzz2zmaU3TvvX6Ho0i0e1kvaVlx23+l9VwwrTDSim2bNkCQPL4xynZcS3xwxcTGn+Afcse4+CKLaRcNoaqEBORTV7Knswh6Tfj/b48VptTSlqNl/cTLNyZ0ptku5VUu40Uu41Yq6WVCW4CjP3fBbXrMVClVJWIrAYuBa4Hjg3a/Q/g5QBFCoATB9tOormpqFRE4n1X//FAxycU1TTtvCEhRl9CnuVQi/XfnTqlxfLKlSsprKkjJz2DzNg3SU2Yd/ySs//0X7P8zZeIq8qlyWZhfFMj4lGU/X4bZoeFyCv7Y+llx1vvpvIto+3/uslpOPp1bDTTruq0CUBEYoAm38nfjnGV/98YJ/JsjLb7KcCeAMU3A2kikgoUArOBub5t72EkkQW+3++e0V+iadp5wV3hwo2HOpq/gZuens6wiUkt9lNK8WVSPwpje2KnAQQiwkdwqDAXa1gTXswUrykG4Bu7iVSbmab8ozQBJXtyMEeFIubmq3trgv9AdOe7ttwBxAOv+voBTMCbSqn3RaQKeFZELIALXzu9iCQALyulpiul3CLyc2AFYAb+rJTa6TvuAuBNEbkJOAjMPJt/mKZp549AzTCXXHIJL7+ynkGFh3Al2gilgfodwrBdL/FlyFoA+n0vmVFDYqjeUgqbS4j+8WBCksI5+tFBGg9W01RiTMuY+NsJLZJBd9GWp4C2AxcEWP8pMDrA+iJg+gnLy4BlAfarAL7bzvpqmnaei7g4meqVB1qs69+/v99+TR7FprwjqDAnNyX9jYfUf3FhQg8acvOJde0nLHsm2dONcnEpEagZAxCTcZKP+kEaXpebokc3YBsY1S1P/gDSlrE2vi0yMzNVTk5OsKuhado5ptxeGusbaBIPSinCwsL87gKOuj2kfbQNLAIi9FXfsMD7C0ZvO0JlTA9Srtt/2vfxNrgBMNnO71FxRGSLUspvXsrz+6/WNK1LEosJW7gd2yn2KWlogpDmL5/le5N5Z83NrE78gjsv/3mb3ud8P/Gfjh4LSNO0LmlxYTkA8YdKQSki67ykVg4jzn050dETg1y7rkEnAE3TuqTbk+OIqKnmb4/cxfVL36Yy3MJ/PJHJbbddG+yqdRk6AWia1iX1toXw0rB+7Jj1Yx64fhYlk0fisMcf7+jVTq97N4BpmtalTUlNgnkPB7saXZa+A9A0TeumdALQNE3rpnQC0DRN66Z0AtA0TeumdALQNE3rpnQC0DRN66Z0AtA0TeumdALQNE3rprrUaKAicgg4cNodv916AeXBrsS3iI6HPx2TlnQ8/LU3JslKqZiTV3apBHA+EJGcQMOydlc6Hv50TFrS8fB3tmKim4A0TdO6KZ0ANE3TuimdADrfH4NdgW8ZHQ9/OiYt6Xj4Oysx0X0AmqZp3ZS+A9A0TeumdALQNE3rpvSEMJ1ARP4ODPItRgJVSqmRImIF/gBkAl7gTqXU6qBUspOdIiYhwMvAKIz/z78opR4PTi07zyni8SPgvhN2HQ6MUkp90bk17HytxcS3bTjGZycC47MzRinlCkI1O80p/kdSgFxgt2/bRqXUrW05pk4AnUApNevYaxF5CjjiW7zZtz1DRGKB5SIyRinlDUI1O9UpYjITsPli4gC+EpElSqm8IFSz07QWD6XU68DrvvUZwLvd4eQPrcdERCzAX4FrlVLbRCQaaApOLTvPKT4zAPuOJcf20AmgE4mIANcAU3yrhgCrAJRSZSJShXE3sCkoFQyCADFRgNP3IbcDjUB1kKrX6QLE40RzgCWdW6PgCxCTS4DtSqltAEqpimDVLRhO8z/SLroPoHNNBEqVUnt8y9uAq0TEIiKpwGigT9BqFxwnx+QtoBYoBg4CTyqlDgerckFwcjxONItumADwj8lAQInIChHZKiK/DGLdgiHQ/0iqiHwuImtEZGJbD6TvAM4SEfkQ6B1g08NKqXd9r0++gvszMBjIwRjjaD3gPpf17EwdjEkW4AESgChgrYh8qJTaf04r2wk6GI9jZccCdUqpHeewip2ugzGxABOAMUAdsEpEtiilVp3TynaCDsajGOirlKoQkdHAOyIyVCl12jtnnQDOEqXU1FNt9zVp/ADjKv9YGTdw9wn7rAcCXfl1SR2JCTAX+EAp1QSUicg6jGaxLp8AOhiPY2ZzHl79dzAmBcAapVS5b59lGA8NdPkE0MHzSAPQ4Hu9RUT2Ydwl5Zzu/XQTUOeZCuxSShUcWyEiDhFx+l5fDLiVUl8Fq4JB4BcTjGafKWJwAhcCu4JSu84XKB6IiAmjc/yNoNQquALFZAUw3Pf5sQDZQHf53AQ6j8SIiNn3uh+QRhsvmPQdQOcJdAUXC6wQES9QCFzb6bUKrkAxeQFYBOwABFiklNre2RULktau8i8CCs6HZrAO8IuJUqpSRJ4GNmM8NLBMKbU0GJULgkD/IxcB80XEjdF8emtb+830UBCapmndlG4C0jRN66Z0AtA0TeumdALQNE3rpnQC0DRN66Z0AtA0TeumdALQNE3rpnQC0DRN66b+H19z63YjlFR6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 2 of 4\n",
    "#visualize the to-be-cut precincts, just to confirm there are really xx precincts up there\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] ==1:\n",
    "        if notPolyVTD[p]==1:\n",
    "            for geom in vtdGeom[p].geoms:\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y)\n",
    "        else:\n",
    "            x,y = vtdGeom[p].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "34f94200-4cc7-4226-9a6c-d6ae75b3380f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 3 of 4\n",
    "#now visualize the receivers\n",
    "for pp in range(len(precinctReceivers)):\n",
    "    p = precinctReceivers[pp]\n",
    "    if notPolyVTD[p]==1:\n",
    "        for geom in vtdGeom[p].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = vtdGeom[p].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "9301c018-ac66-4a64-8efe-a839a349e53f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now visualize the TRACT receivers\n",
    "for tt in range(len(tractReceivers)):\n",
    "    t = tractReceivers[tt]\n",
    "    if notPoly[t]==1:\n",
    "        for geom in tractGeom[t].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = tractGeom[t].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e8d4b939-2d62-4d7a-a866-7283b3e8e383",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have finished slicing  263227.0 people (VAP of 201461.0 ) and  133760.0 voters out.\n"
     ]
    }
   ],
   "source": [
    "#now, cut and redistribute\n",
    "cutPop = 0.\n",
    "cutVAP = 0.\n",
    "cutHisp = 0.\n",
    "cutBlack = 0.\n",
    "cutTrump = 0.\n",
    "cutBiden = 0.\n",
    "\n",
    "for ct in range(len(cutTractList)):\n",
    "    t = cutTractList[ct]\n",
    "    cutPop += tractPop[t]  #sum up the cut pops\n",
    "    cutVAP += tractVAP[t]\n",
    "    cutHisp += tractHisp[t]\n",
    "    cutBlack += tractBlack[t]\n",
    "    # Now we zero out the pops in the cut tracts\n",
    "    tractPop[t] = 0\n",
    "    tractVAP[t] = 0\n",
    "    tractHisp[t] = 0\n",
    "    tractBlack[t] = 0\n",
    "# Now distribute the pops among the receivers\n",
    "ntR = len(tractReceivers)\n",
    "NTR = float(ntR)\n",
    "for rt in range(ntR) :\n",
    "    t = tractReceivers[rt]\n",
    "    tractPop[t] += cutPop/NTR\n",
    "    tractVAP[t] += cutVAP/NTR\n",
    "    tractHisp[t] += cutHisp/NTR\n",
    "    tractBlack[t] += cutBlack/NTR\n",
    "\n",
    "#             now do the same for the precincts\n",
    "for cp in range(len(cutPrecinctList)):\n",
    "    p = cutPrecinctList[cp]\n",
    "    cutTrump += vtdTrump[p]\n",
    "    cutBiden += vtdBiden[p]\n",
    "        # Now we zero out the pops in the cut precincts\n",
    "    vtdTrump[p] = 0\n",
    "    vtdBiden[p] = 0\n",
    "\n",
    "# Now distribute the pops among the receivers\n",
    "npR = len(precinctReceivers)\n",
    "NPR = float(npR)\n",
    "for rp in range(npR) :\n",
    "    p = precinctReceivers[rp]\n",
    "    vtdTrump[p] += cutTrump/NPR\n",
    "    vtdBiden[p] += cutBiden/NPR\n",
    "print(\"I have finished slicing \",cutPop,\"people (VAP of\",cutVAP,\") and \",cutBiden+cutTrump,\"voters out.\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "84c2cf09-b3ea-4fc2-a55b-62985882836b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on tract 50\n",
      "working on tract 100\n",
      "working on tract 150\n",
      "working on tract 200\n",
      "working on tract 250\n",
      "working on tract 300\n",
      "working on tract 350\n",
      "working on tract 400\n",
      "working on tract 450\n",
      "working on tract 500\n",
      "working on tract 550\n",
      "working on tract 600\n",
      "working on tract 650\n",
      "working on tract 700\n",
      "working on tract 750\n",
      "working on tract 800\n",
      "working on tract 850\n",
      "working on tract 900\n",
      "working on tract 950\n",
      "working on tract 1000\n",
      "working on tract 1050\n",
      "working on tract 1100\n",
      "working on tract 1150\n",
      "working on tract 1200\n",
      "working on tract 1250\n",
      "working on tract 1300\n",
      "working on tract 1350\n",
      "working on tract 1400\n",
      "working on tract 1450\n",
      "working on tract 1500\n",
      "working on tract 1550\n",
      "working on tract 1600\n",
      "working on tract 1650\n",
      "working on tract 1700\n",
      "working on tract 1750\n",
      "working on tract 1800\n",
      "working on tract 1850\n",
      "working on tract 1900\n",
      "working on tract 1950\n",
      "working on tract 2000\n",
      "working on tract 2050\n",
      "working on tract 2100\n",
      "working on tract 2150\n",
      "working on tract 2200\n",
      "working on tract 2250\n",
      "working on tract 2300\n",
      "working on tract 2350\n",
      "working on tract 2400\n",
      "working on tract 2450\n",
      "working on tract 2500\n",
      "working on tract 2550\n",
      "working on tract 2600\n",
      "working on tract 2650\n",
      "working on tract 2700\n",
      "working on tract 2750\n",
      "working on tract 2800\n",
      "working on tract 2850\n",
      "working on tract 2900\n",
      "working on tract 2950\n",
      "working on tract 3000\n",
      "working on tract 3050\n",
      "working on tract 3100\n",
      "working on tract 3150\n",
      "working on tract 3200\n",
      "working on tract 3250\n",
      "working on tract 3300\n",
      "working on tract 3350\n",
      "working on tract 3400\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#rebuild tractMAP, excluding skipped / sliced tracts.  #NOT NEEDED FOR OHIO -- NO SLICED populous TRACTS\n",
    "counter = -1 #Watchout - low-number tracts may have been skipped\n",
    "found = \"no\"\n",
    "while found == \"no\":\n",
    "    counter +=1\n",
    "    if isSkippedTract[counter]==0:\n",
    "        starter = counter\n",
    "        found = \"yes\"\n",
    "\n",
    "tractMAP = tractGeom[starter]\n",
    "for t in range(starter+1,nTracts):\n",
    "    if t%200 == 0:\n",
    "        print(\"working on tract\",t)\n",
    "    if isSkippedTract[t] == 0:\n",
    "        tractMAP = tractMAP.union(tractGeom[t])\n",
    "\n",
    "if type(tractMAP) != type(tractGeom[1]):\n",
    "    for geom in tractMAP.geoms:\n",
    "        x,y = geom.exterior.xy\n",
    "        plt.plot(x,y)\n",
    "else:\n",
    "    x,y = tractMAP.exterior.xy\n",
    "    plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d4538dfc-7d35-46b4-95c1-c5d93169e971",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "650cf37d-e11a-4ed3-861b-3f3edc411422",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our distribution of precinct's Trump votes by precinct Biden votes?\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(xlabel=\"Biden votes in this precinct\", ylabel=\"Trump precinct votes\")\n",
    "plt.plot(vtdBiden, vtdTrump, marker='.',linestyle=\"none\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "d40408f3-31eb-4b8a-ad91-4e2eb5f422df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0.7157676348547718 482\n",
      "100 0.4222972972972973 592\n",
      "200 0.5105401844532279 1518\n",
      "300 0.26401869158878505 428\n",
      "400 0.5015337423312883 1956\n",
      "500 0.20973782771535582 267\n",
      "600 0.5106382978723404 47\n",
      "700 0.6287170773152082 1177\n",
      "800 0.22304832713754646 538\n",
      "900 0.6238859180035651 561\n",
      "1000 0.0 0\n",
      "1100 0.7166172106824926 2696\n",
      "1200 0.717948717948718 780\n",
      "1300 0.7537154989384289 471\n",
      "1400 0.4274744027303754 1172\n",
      "1500 0.6659729448491155 961\n",
      "1600 0.7693726937269373 542\n",
      "1700 0.6565874730021598 1852\n",
      "1800 0.754356846473029 1205\n",
      "1900 0.5520833333333334 1632\n",
      "2000 0.4910277324632953 613\n",
      "2100 0.8295454545454546 176\n",
      "2200 0.8209255533199196 497\n",
      "2300 0.5128865979381443 1164\n",
      "2400 0.7213270142180095 1055\n",
      "2500 0.7960308710033076 907\n",
      "2600 0.3979591836734694 588\n",
      "2700 0.5323579065841305 1777\n",
      "2800 0.4052044609665427 807\n",
      "2900 0.7439024390243902 82\n",
      "3000 0.24746450304259635 986\n",
      "3100 0.35288552507095555 1057\n",
      "3200 0.36146020347097546 1671\n",
      "3300 0.26143790849673204 1530\n",
      "3400 0.5497630331753555 633\n",
      "3500 0.20351758793969849 398\n",
      "3600 0.0165016501650165 303\n",
      "3700 0.03103448275862069 290\n",
      "3800 0.013513513513513514 370\n",
      "3900 0.016018306636155607 437\n",
      "4000 0.2564841498559078 347\n",
      "4100 0.030456852791878174 197\n",
      "4200 0.07103825136612021 549\n",
      "4300 0.14102564102564102 468\n",
      "4400 0.33766233766233766 385\n",
      "4500 0.02925531914893617 376\n",
      "4600 0.04398826979472141 341\n",
      "4700 0.3242320819112628 293\n",
      "4800 0.401840490797546 326\n",
      "4900 0.08089887640449438 445\n",
      "5000 0.5870967741935483 465\n",
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    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ALL TRIMS COMPLETE. let's convert the partisan data to the format we use for HD code\n",
    "# in this prototype, we only use 2020 presidential data, but this could be modified\n",
    "vtdGOP = [0.]*nPrecincts  #Each precinct's R/(R+D)\n",
    "vtdPop = [0.]*nPrecincts   #THIS IS VOTER POPULATION\n",
    "vtdArea = [0.]*nPrecincts\n",
    "vtdCPx = [0.]*nPrecincts   #these are centroid x and y of each precinct\n",
    "vtdCPy = [0.]*nPrecincts\n",
    "plotcolor = ['blue']*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    vtdPop[p] = vtdTrump[p] + vtdBiden[p] \n",
    "    vtdGOP[p] = vtdTrump[p]/max((vtdTrump[p] + vtdBiden[p]), 0.01)  #avoid divide by zero\n",
    "    if (vtdGOP[p] > 0.40) :\n",
    "        plotcolor[p]='purple'\n",
    "        if (vtdGOP[p] > 0.60) :\n",
    "            plotcolor[p] = 'red'\n",
    "    vtdArea[p] = vtdGeom[p].area\n",
    "    vtdCPx[p] = vtdGeom[p].centroid.x  #not needed except for graphing\n",
    "    vtdCPy[p] = vtdGeom[p].centroid.y   #not needed  \"  \"  \"\n",
    "    if isSkippedPrecinct[p] == 0 and p%10 == 0: #memory drain if we print all\n",
    "        plt.scatter(vtdCPx[p], vtdCPy[p], marker='.',c=plotcolor[p])\n",
    "for p in range (nPrecincts):\n",
    "    if p%100 == 0:\n",
    "        print(p,vtdGOP[p],vtdPop[p])\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "a1e3f0d2-c21c-46d4-8227-e75dfd5e89b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "original and final map areas are 12.535917822993019 12.51063536906052\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "MAP = tractMAP  #committing to the trimmed map (for TN, using convex hull)\n",
    "#bufferDistance = 0.02  #do minor buffer for OH\n",
    "origMAP = wholeMAP    # let's keep a copy of the original map prior to buffering\n",
    "#MAPbuffer = MAP.exterior.buffer(bufferDistance, single_sided=True)  #gives a little exterior buffer\n",
    "# MAP = origMAP.convex_hull  #alternate to buffer for weird state shapes\n",
    "bBox = MAP.bounds\n",
    "MAPMinX = bBox[0]  #these four are useful for slicing ops, and are basis for map grid (without buffer)\n",
    "MAPMinY = bBox[1]\n",
    "MAPMaxX = bBox[2]\n",
    "MAPMaxY = bBox[3]\n",
    "\n",
    "#MAP = MAP.union(MAPbuffer)  #not buffering for TN\n",
    "print(\"original and final map areas are\",origMAP.area,MAP.area)  #same if we didn't buffer\n",
    "x2, y2 = MAP.exterior.xy\n",
    "plt.plot(x2,y2,c=\"green\")\n",
    "x2, y2 = origMAP.exterior.xy\n",
    "plt.plot(x2,y2,c=\"blue\")\n",
    "plt.show()\n",
    "\n",
    "minTractPop = 10  #was zero to not exclude empty interiors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "8e271f55-e118-4575-bc99-c28c59f435b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020 population, Trump+Biden voters, lean =  13002690 6838872 0.4940024860661418\n",
      "compare to Census 13002700 Leip has Trump + Biden = 6840276.0 0.49399395579944433\n"
     ]
    }
   ],
   "source": [
    "#let's confirm some population stats - these match 2020 census, USelectionatlas.org :-)\n",
    "censusPop = 13002700\n",
    "atlasTrump = 3379055.\n",
    "atlasBiden = 3461221.\n",
    "\n",
    "print(\"2020 population, Trump+Biden voters, lean = \",np.sum(tractPop),np.sum(vtdPop),stateGOP )\n",
    "print(\"compare to Census\",censusPop,\"Leip has Trump + Biden =\",atlasTrump+atlasBiden,\n",
    "      atlasTrump/(atlasTrump+atlasBiden) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "6c47f717-f208-4a4c-b825-a3181a83c6ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "state pop before, after slice operation are 13002690 13002690.0\n",
      "state Trumpers before, after slice opn are 3378419 3378419.0\n",
      "state Bideners before, after slice opn are 3460453 3460453.0\n"
     ]
    }
   ],
   "source": [
    "#Check on integrity after slicing\n",
    "sumPop = 0.\n",
    "sumTrump = 0.\n",
    "sumBiden = 0.\n",
    "for t in range(nTracts):\n",
    "    if isSkippedTract[t] == 0:\n",
    "        sumPop += tractPop[t]\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] == 0:\n",
    "        sumTrump += vtdTrump[p]\n",
    "        sumBiden += vtdBiden[p]\n",
    "print(\"state pop before, after slice operation are\",np.sum(tractPop),sumPop)\n",
    "print(\"state Trumpers before, after slice opn are\",np.sum(vtdTrump),sumTrump)\n",
    "print(\"state Bideners before, after slice opn are\",np.sum(vtdBiden),sumBiden)\n",
    "for t in range(nTracts):\n",
    "    if isSkippedTract[t] ==1 and tractPop[t] > 0:  #should not occur\n",
    "        print(\"missed pop for tract, pop, (x,y)\",t,tractPop[t],tractGeom[t].centroid.x,\n",
    "              tractGeom[t].centroid.y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "b5334311-b597-4568-88c7-eb3c9d4bdf09",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "map has a total of 144 grids for 17 districts\n",
      "starting grid no 0 at x = -80.35755433333331, y = 39.87966937499999 \n",
      "starting grid no 5 at x = -80.35755433333331, y = 41.47837312499998 \n",
      "starting grid no 10 at x = -80.03366899999999, y = 40.51915087500001 \n",
      "starting grid no 15 at x = -80.03366899999999, y = 42.11785462499999 \n",
      "starting grid no 20 at x = -79.70978366666667, y = 41.158632374999996 \n",
      "starting grid no 25 at x = -79.38589833333333, y = 40.19941012499999 \n",
      "starting grid no 30 at x = -79.38589833333333, y = 41.79811387499999 \n",
      "starting grid no 35 at x = -79.06201300000002, y = 40.838891624999995 \n",
      "starting grid no 40 at x = -78.73812766666667, y = 39.87966937499999 \n",
      "starting grid no 45 at x = -78.73812766666667, y = 41.47837312499998 \n",
      "starting grid no 50 at x = -78.41424233333335, y = 40.519150875 \n",
      "starting grid no 55 at x = -78.41424233333335, y = 42.11785462499999 \n",
      "starting grid no 60 at x = -78.09035700000001, y = 41.158632374999996 \n",
      "starting grid no 65 at x = -77.76647166666666, y = 40.19941012499999 \n",
      "starting grid no 70 at x = -77.76647166666666, y = 41.79811387499999 \n",
      "starting grid no 75 at x = -77.44258633333334, y = 40.838891624999995 \n",
      "starting grid no 80 at x = -77.11870099999999, y = 39.87966937499999 \n",
      "starting grid no 85 at x = -77.11870099999999, y = 41.47837312499998 \n",
      "starting grid no 90 at x = -76.79481566666668, y = 40.519150875 \n",
      "starting grid no 95 at x = -76.79481566666668, y = 42.11785462499999 \n",
      "starting grid no 100 at x = -76.47093033333334, y = 41.158632374999996 \n",
      "starting grid no 105 at x = -76.14704499999999, y = 40.19941012499999 \n",
      "starting grid no 110 at x = -76.14704499999999, y = 41.79811387499999 \n",
      "starting grid no 115 at x = -75.82315966666667, y = 40.838891624999995 \n",
      "starting grid no 120 at x = -75.49927433333333, y = 39.87966937499999 \n",
      "starting grid no 125 at x = -75.49927433333333, y = 41.47837312499999 \n",
      "starting grid no 130 at x = -75.175389, y = 40.51915087500001 \n",
      "starting grid no 135 at x = -75.175389, y = 42.11785462499999 \n",
      "starting grid no 140 at x = -74.85150366666666, y = 41.158632374999996 \n",
      "done building grids. avgGridDensity, maxGridDensity,nPopulatedGrids = 890481.3425240028 14482449.435020143 141.0\n"
     ]
    }
   ],
   "source": [
    "# this section - ESTIMATE POP'N DENSITY at scale of 1/3 of district length\n",
    "# also, IDENTIFY WHICH TRACTS and VTD's INTERSECT EACH GRID to speed later intersection searches by grid \n",
    "# (above sections already pulled in Tract and Precinct geometry and demographic data, built an overall map)\n",
    "nDistricts = 17   #PA congressional\n",
    "avgDistrictPop = np.sum(tractPop) / float(nDistricts)\n",
    "#MAPMaxX = -80.   #In most state maps, we figure this out from the bounding box of the convex hull; see above.\n",
    "#MAPMaxY = 31.1   # ... but for Florida, we will do manually\n",
    "#MAPMinX = -87.0\n",
    "#MAPMinY = 25.5\n",
    "stateWidth = float(MAPMaxX - MAPMinX)\n",
    "stateHeight = float(MAPMaxY - MAPMinY)\n",
    "stateWHRatio = stateWidth / stateHeight\n",
    "G = 3  #adjustable parameter; G*G = number of grids that fit in an average district\n",
    "nGridsX = int( G*round((nDistricts*stateWHRatio)**0.5,0) )   #OK to have empty grids for a non-rectglr state\n",
    "nGridsY = int(round(nGridsX / stateWHRatio,0) )  #so grids are square even if state has high aspect ratio\n",
    "nGrids = int(nGridsX*nGridsY)\n",
    "print(\"map has a total of {0} grids for {1} districts\".format(nGrids,nDistricts) )\n",
    "nGridPrecincts = [0]*nGrids # will store how many precincts intersect with each grid square\n",
    "nGridTracts = [0]*nGrids    # same, but for tracts\n",
    "gridPrecinctNo = [[0]*nPrecincts for gridNo in range(nGrids) ] # initialize a list of VTDs that intersect with each grid square\n",
    "gridTractNo = [[0]*nTracts for gridNo in range(nGrids) ] # initialize a list of tracts that intersect with each grid square\n",
    "\n",
    "gridPop = [0.]*nGrids #will store approx total population for this grid square\n",
    "gridDensity = [0.]*nGrids\n",
    "gridGeom = [Polygon([(0,0),(0,1),(1,0)])]*nGrids  #initialize grid geometry\n",
    "gridWidth = stateWidth /nGridsX        #geo length of a grid's side length\n",
    "gridHeight = stateHeight /nGridsY    \n",
    "\n",
    "for nG in range (nGrids) : #  create polygon shape for each grid square, compute grid-square population density\n",
    "    x = int(nG/nGridsY)\n",
    "    y = int(nG % nGridsY)\n",
    "    point1 = Point(x*gridWidth+MAPMinX,y*gridHeight+MAPMinY)\n",
    "    point2 = Point((x+1)*gridWidth+MAPMinX, y*gridHeight+MAPMinY)\n",
    "    point3 = Point((x+1)*gridWidth+MAPMinX, (y+1)*gridHeight+MAPMinY)\n",
    "    point4 = Point(x*gridWidth+MAPMinX, (y+1)*gridHeight+MAPMinY)\n",
    "    gridGeom[nG] = Polygon([point1, point2, point3, point4])\n",
    "    if (nG %5 == 0): #print occasionally, should take about one second per grid\n",
    "        print(\"starting grid no {0} at x = {1}, y = {2} \".format(nG,gridGeom[nG].centroid.x,gridGeom[nG].centroid.y) )\n",
    "    counter = 0\n",
    "    #    Now for each grid square, find intersxn with all polygons, total the intersection population\n",
    "    #    Also, create 2 lists: of TRACTS and precincts that intersect each grid (efficiency shortcut)\n",
    "    \n",
    "    if( gridGeom[nG].intersection(MAP).area > 0.0001) : #don't bother w grids off the map\n",
    "        for t in range(nTracts):\n",
    "            # if (t%3000 == 0) :\n",
    "            #    print (\"grid {0}, tract no {1}, ({2},{3})\".format(nG,t,tractGeom[t].centroid.x,tractGeom[t].centroid.y) )\n",
    "            intersxnArea = 0.\n",
    "            if (notPoly[t] == 0) :  # tract is a simple polygon\n",
    "                intersxnArea = gridGeom[nG].intersection(tractGeom[t]).area  #replaced tractGeom w cleanedPoly\n",
    "            else : #tract is a multiPolygon, do the polygon intersections individually\n",
    "                #print(\"I think tract\",t,\"is a multiPolygon\")\n",
    "                for geom in tractGeom[t].geoms :\n",
    "                    intersxnArea += gridGeom[nG].intersection(geom).area\n",
    "            if (intersxnArea > 0 and tractPop[t] > minTractPop) : # this tract is at least partially in this grid and populated \n",
    "                if (tractArea[t] == 0):  #should not happen, but debugging\n",
    "                    print(t,tractGeom[t].centroid.x, tractGeom[t].centroid.y)  #debug print\n",
    "                gridPop[nG] += tractPop[t]*intersxnArea/tractArea[t]\n",
    "                if tractPop[t] > (1.0 * avgDistrictPop) :  #flag if we have a mega tract\n",
    "                    uhoh = input(\"Uh oh! We have a tract that's bigger than a district.  What now?\")\n",
    "                else :\n",
    "                    gridTractNo[nG][counter] = t  #add this tract to this grid's list, update the no of tracts in grid\n",
    "                    counter +=1            \n",
    "                    nGridTracts[nG] = counter\n",
    "        # OK, now, loop over PRECINCTS to develop each grid's list of precincts (prev loop was for tracts)\n",
    "        counter = 0\n",
    "        for p in range(nPrecincts):\n",
    "            intersxnArea = 0.\n",
    "            if (notPolyVTD[p] == 0) :  # precinct is a simple polygon\n",
    "                intersxnArea = gridGeom[nG].intersection(vtdGeom[p]).area  #replaced tractGeom w cleanedPoly\n",
    "            else : #precinct is a multiPolygon, do the polygon intersections individually\n",
    "                for geom in vtdGeom[p].geoms :\n",
    "                    intersxnArea += gridGeom[nG].intersection(geom).area\n",
    "            intersxnArea = gridGeom[nG].intersection(vtdGeom[p]).area\n",
    "            if (intersxnArea > 0 and vtdPop[p] >0) : # this precinct is at least partially in this grid and recorded votes                 \n",
    "                gridPrecinctNo[nG][counter] = p  #add this precinct to this grid's list, update the no of tracts in grid\n",
    "                counter +=1            \n",
    "                nGridPrecincts[nG] = counter\n",
    "    gridDensity[nG] = gridPop[nG] / gridGeom[nG].area  #finished loop over tracts & vtd's.  Calceach grid's population density    \n",
    "\n",
    "minGridPop = np.min(gridPop)\n",
    "# avgGridPop = np.mean(gridPop)   #see below for explicit averaging, since there are so many empty grids\n",
    "maxGridPop = np.max(gridPop)\n",
    "nPopulatedGrids = 0.\n",
    "totPopulatedGridArea = 0.\n",
    "for nG in range(nGrids) :\n",
    "    if gridPop[nG] > 0. :\n",
    "        nPopulatedGrids +=1\n",
    "        totPopulatedGridArea += gridGeom[nG].area\n",
    "avgGridPop = np.sum(tractPop) / nPopulatedGrids\n",
    "avgGridDensity = np.sum(tractPop) / totPopulatedGridArea\n",
    "#avgGridDensity = np.average(gridDensity) #avgGridPop / (gridPL * gridPL) #these densities help home district algorithm\n",
    "maxGridDensity = np.max(gridDensity)\n",
    "print(\"done building grids. avgGridDensity, maxGridDensity,nPopulatedGrids =\",avgGridDensity,maxGridDensity,nPopulatedGrids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "808f513f-21af-4b03-8061-fdeeed33dafd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a heat map of grid density \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"here is a heat map of grid density \")\n",
    "xyGridDensity = [[0.]*nGridsX for x in range (nGridsY) ]   #flipping x and y to get right orientation in pixel grid\n",
    "for nG in range (nGrids) : #  create polygon shape for each grid square, compute grid-square population density\n",
    "    x = int(nG % nGridsY)\n",
    "    y = int(nG / nGridsY)\n",
    "    if gridDensity[nG] > 0 :  #avoid log zero\n",
    "        xyGridDensity[x][y]=np.log(gridDensity[nG])\n",
    "c=plt.pcolormesh(xyGridDensity)\n",
    "plt.colorbar(c)\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "4013f80b-4fc8-40a1-b708-ee29c29b3ffe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.2076999999966126e-06 7.613059499996898e-06\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(np.min(vtdArea), np.min(tractArea))\n",
    "minTractPop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "ab450500-f1a4-4292-a2fe-71eb2217121d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I am working on tract number 0 of 3446 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 19 3.0 3 -272.4 0.8629 192744.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 19 4.0 3 -272.4 0.8578 192744.8\n",
      "I am working on tract number 20 of 3446 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 36 2 195240.21834706885 2.6905\n",
      "8 yoyos for tract,wedge,wedgePop,r= 36 2 48069.85452224387 1.3453\n",
      "9 yoyos for tract,wedge,wedgePop,r= 36 2 321796.2915013117 2.7507\n",
      "10 yoyos for tract,wedge,wedgePop,r= 36 2 108969.72181098204 2.048\n",
      "10 yoyos for tract,wedge,wedgePop,r= 36 2 126007.7596508695 2.3993\n",
      "10 yoyos for tract,wedge,wedgePop,r= 36 2 137601.36180409198 2.575\n",
      "10 yoyos for tract,wedge,wedgePop,r= 36 2 159193.15577185235 2.6628\n",
      "11 yoyos for tract,wedge,wedgePop,r= 36 2 232303.42139262185 2.7068\n",
      "12 yoyos for tract,wedge,wedgePop,r= 36 2 184676.20716374385 2.6848\n",
      "13 yoyos for tract,wedge,wedgePop,r= 36 2 205948.0990083895 2.6958\n",
      "I am working on tract number 40 of 3446 tracts\n",
      "I am working on tract number 60 of 3446 tracts\n",
      "I am working on tract number 80 of 3446 tracts\n",
      "I am working on tract number 100 of 3446 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 115\n",
      "I am working on tract number 120 of 3446 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 122\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 125 12.0 0 131.2 1.7219 627038.8\n",
      "we have 2 non-opposing shorted wedges for tract no 126\n",
      "we have 2 non-opposing shorted wedges for tract no 127\n",
      "we have 2 non-opposing shorted wedges for tract no 128\n",
      "we have 2 non-opposing shorted wedges for tract no 129\n",
      "we have 2 non-opposing shorted wedges for tract no 134\n",
      "I am working on tract number 140 of 3446 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 157 7.0 0 90.0 1.0208 294421.4\n",
      "I am working on tract number 160 of 3446 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 175\n",
      "I am working on tract number 180 of 3446 tracts\n",
      "I am working on tract number 200 of 3446 tracts\n",
      "I am working on tract number 220 of 3446 tracts\n",
      "I am working on tract number 240 of 3446 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 256 4.0 2 90.0 2.3787 256691.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 256 9.0 2 90.0 2.2089 256691.1\n",
      "I am working on tract number 260 of 3446 tracts\n",
      "I am working on tract number 280 of 3446 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 292 2.0 3 90.0 0.755 219375.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 292 3.0 3 90.0 0.6799 219375.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 292 4.0 3 90.0 0.6122 219375.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 292 5.0 3 90.0 0.5512 219375.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 292 6.0 3 90.0 0.4963 219375.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 292 7.0 3 90.0 0.4469 219375.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 292 8.0 3 90.0 0.4024 219375.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 292 9.0 3 90.0 0.3623 219375.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 292 10.0 3 90.0 0.3263 219375.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 292 11.0 3 90.0 0.2938 219375.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 292 12.0 3 90.0 0.2645 219375.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 292 13.0 3 90.0 0.2382 219375.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 292 14.0 3 90.0 0.2145 219375.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 292 15.0 3 90.0 0.1931 219375.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 292 16.0 3 90.0 0.1739 219375.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 292 17.0 3 90.0 0.1566 219375.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 292 18.0 3 90.0 0.141 219375.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 292 19.0 3 90.0 0.1269 219375.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 292 20.0 3 90.0 0.1143 219375.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 292 21.0 3 90.0 0.1029 219375.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 292 22.0 3 90.0 0.0927 219375.0\n",
      "I am working on tract number 300 of 3446 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 313 3.0 0 90.0 1.0296 223489.8\n",
      "I am working on tract number 320 of 3446 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 322\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 324 8.0 3 54.1 1.0508 268464.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 324 13.0 3 54.1 1.13 268464.4\n",
      "7 yoyos for tract,wedge,wedgePop,r= 324 3 268064.6220480504 0.9932\n",
      "8 yoyos for tract,wedge,wedgePop,r= 324 3 29959.819602447766 0.4966\n",
      "9 yoyos for tract,wedge,wedgePop,r= 324 3 268464.4385652236 1.0877\n",
      "7 yoyos for tract,wedge,wedgePop,r= 329 3 317650.3443262633 1.0949\n",
      "8 yoyos for tract,wedge,wedgePop,r= 329 3 50963.267087117885 0.5475\n",
      "9 yoyos for tract,wedge,wedgePop,r= 329 3 317803.3087055491 1.1087\n",
      "10 yoyos for tract,wedge,wedgePop,r= 329 3 118311.90853800336 0.8281\n",
      "11 yoyos for tract,wedge,wedgePop,r= 329 3 299362.64842800517 0.9684\n",
      "12 yoyos for tract,wedge,wedgePop,r= 329 3 186326.01120214484 0.8982\n",
      "13 yoyos for tract,wedge,wedgePop,r= 329 3 257391.56843217497 0.9333\n",
      "I am working on tract number 340 of 3446 tracts\n",
      "I am working on tract number 360 of 3446 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 371 3.0 2 90.0 1.4704 193652.3\n",
      "I am working on tract number 380 of 3446 tracts\n",
      "I am working on tract number 400 of 3446 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 417 4.0 1 90.0 1.2081 194904.6\n",
      "I am working on tract number 420 of 3446 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 420 15.0 0 122.0 1.1012 358675.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 433 6.0 3 39.9 0.1736 198762.3\n",
      "we have 2 non-opposing shorted wedges for tract no 438\n",
      "I am working on tract number 440 of 3446 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 440\n",
      "I am working on tract number 460 of 3446 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 461\n",
      "we have 2 non-opposing shorted wedges for tract no 467\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 472 5.0 0 140.3 1.3701 295781.4\n",
      "I am working on tract number 480 of 3446 tracts\n",
      "I am working on tract number 500 of 3446 tracts\n",
      "I am working on tract number 520 of 3446 tracts\n",
      "I am working on tract number 540 of 3446 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 549 4.0 2 90.0 1.5851 221809.4\n",
      "I am working on tract number 560 of 3446 tracts\n",
      "I am working on tract number 580 of 3446 tracts\n",
      "I am working on tract number 600 of 3446 tracts\n",
      "I am working on tract number 620 of 3446 tracts\n",
      "I am working on tract number 640 of 3446 tracts\n",
      "I am working on tract number 660 of 3446 tracts\n",
      "I am working on tract number 680 of 3446 tracts\n",
      "I am working on tract number 700 of 3446 tracts\n",
      "I am working on tract number 720 of 3446 tracts\n",
      "I am working on tract number 740 of 3446 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 755 4.0 2 90.0 0.9982 205570.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 755 5.0 2 90.0 0.945 205570.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 755 6.0 2 90.0 0.8946 205570.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 755 7.0 2 90.0 0.8469 205570.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 755 8.0 2 90.0 0.8018 205570.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 755 9.0 2 90.0 0.759 205570.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 755 10.0 2 90.0 0.7185 205570.2\n",
      "I am working on tract number 760 of 3446 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 763\n",
      "7 yoyos for tract,wedge,wedgePop,r= 775 0 265608.73796145705 1.4793\n",
      "8 yoyos for tract,wedge,wedgePop,r= 775 0 26084.920656919072 0.7397\n",
      "9 yoyos for tract,wedge,wedgePop,r= 775 0 266971.52266022895 1.5068\n",
      "10 yoyos for tract,wedge,wedgePop,r= 775 0 89008.09484033543 1.1232\n",
      "I am working on tract number 780 of 3446 tracts\n",
      "I am working on tract number 800 of 3446 tracts\n",
      "I am working on tract number 820 of 3446 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 826 7.0 0 110.2 0.1591 178443.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 826 8.0 0 110.2 0.1675 178443.7\n",
      "I am working on tract number 840 of 3446 tracts\n",
      "I am working on tract number 860 of 3446 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 865\n",
      "we have 2 non-opposing shorted wedges for tract no 866\n",
      "I am working on tract number 880 of 3446 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 893 3.0 2 90.0 1.4413 207154.1\n",
      "I am working on tract number 900 of 3446 tracts\n",
      "I am working on tract number 920 of 3446 tracts\n",
      "I am working on tract number 940 of 3446 tracts\n",
      "I am working on tract number 960 of 3446 tracts\n",
      "I am working on tract number 980 of 3446 tracts\n",
      "I am working on tract number 1000 of 3446 tracts\n",
      "I am working on tract number 1020 of 3446 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1026\n",
      "I am working on tract number 1040 of 3446 tracts\n",
      "I am working on tract number 1060 of 3446 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1063\n",
      "we have 2 non-opposing shorted wedges for tract no 1064\n",
      "we have 2 non-opposing shorted wedges for tract no 1074\n",
      "I am working on tract number 1080 of 3446 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1085\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1089 0 328062.7009624917 0.9549\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1089 0 87136.39951728328 0.4775\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1089 0 324907.19711228134 0.8607\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1089 0 288984.5763838361 0.6691\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1089 0 123057.51521089289 0.5733\n",
      "I am working on tract number 1100 of 3446 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1113\n",
      "I am working on tract number 1120 of 3446 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1138\n",
      "I am working on tract number 1140 of 3446 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1140\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1147 12.0 0 96.4 0.1289 181311.6\n",
      "we have 2 non-opposing shorted wedges for tract no 1148\n",
      "I am working on tract number 1160 of 3446 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1171 2.0 3 90.0 0.5084 797659.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1172 3.0 1 36.0 0.5622 788814.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1179 3.0 1 83.7 0.5119 781811.2\n",
      "I am working on tract number 1180 of 3446 tracts\n",
      "I am working on tract number 1200 of 3446 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1217\n",
      "I am working on tract number 1220 of 3446 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1236\n",
      "I am working on tract number 1240 of 3446 tracts\n",
      "I am working on tract number 1260 of 3446 tracts\n",
      "I am working on tract number 1280 of 3446 tracts\n",
      "I am working on tract number 1300 of 3446 tracts\n",
      "I am working on tract number 1320 of 3446 tracts\n",
      "I am working on tract number 1340 of 3446 tracts\n",
      "I am working on tract number 1360 of 3446 tracts\n",
      "I am working on tract number 1380 of 3446 tracts\n",
      "I am working on tract number 1400 of 3446 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1400\n",
      "we have 2 non-opposing shorted wedges for tract no 1401\n",
      "we have 2 non-opposing shorted wedges for tract no 1403\n",
      "I am working on tract number 1420 of 3446 tracts\n",
      "I am working on tract number 1440 of 3446 tracts\n",
      "I am working on tract number 1460 of 3446 tracts\n",
      "I am working on tract number 1480 of 3446 tracts\n",
      "I am working on tract number 1500 of 3446 tracts\n",
      "I am working on tract number 1520 of 3446 tracts\n",
      "I am working on tract number 1540 of 3446 tracts\n",
      "I am working on tract number 1560 of 3446 tracts\n",
      "I am working on tract number 1580 of 3446 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1599\n",
      "I am working on tract number 1600 of 3446 tracts\n",
      "I am working on tract number 1620 of 3446 tracts\n",
      "I am working on tract number 1640 of 3446 tracts\n",
      "I am working on tract number 1660 of 3446 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1668\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1671 4.0 0 135.4 1.4766 216698.6\n",
      "we have 2 non-opposing shorted wedges for tract no 1673\n",
      "I am working on tract number 1680 of 3446 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1694 3.0 0 83.3 0.3707 213601.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1694 4.0 0 83.3 0.3408 213601.7\n",
      "I am working on tract number 1700 of 3446 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1700 8.0 3 90.0 0.7217 221741.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1700 13.0 3 90.0 0.7334 221741.2\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1700 3 221741.1999545191 0.7919\n",
      "we have 2 non-opposing shorted wedges for tract no 1710\n",
      "we have 2 non-opposing shorted wedges for tract no 1712\n",
      "we have 2 non-opposing shorted wedges for tract no 1716\n",
      "I am working on tract number 1720 of 3446 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1738\n",
      "I am working on tract number 1740 of 3446 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1755\n",
      "I am working on tract number 1760 of 3446 tracts\n",
      "I am working on tract number 1780 of 3446 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1782\n",
      "we have 2 non-opposing shorted wedges for tract no 1791\n",
      "we have 2 non-opposing shorted wedges for tract no 1799\n",
      "I am working on tract number 1800 of 3446 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1803\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1810 3.0 1 79.1 0.5542 283445.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1810 4.0 1 79.1 0.42 283445.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1810 5.0 1 79.1 0.3183 283445.9\n",
      "I am working on tract number 1820 of 3446 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1836\n",
      "I am working on tract number 1840 of 3446 tracts\n",
      "I am working on tract number 1860 of 3446 tracts\n",
      "I am working on tract number 1880 of 3446 tracts\n",
      "I am working on tract number 1900 of 3446 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1913 8.0 0 90.8 1.1714 198065.0\n",
      "I am working on tract number 1920 of 3446 tracts\n",
      "I am working on tract number 1940 of 3446 tracts\n",
      "I am working on tract number 1960 of 3446 tracts\n",
      "I am working on tract number 1980 of 3446 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1993 2.0 2 90.0 0.4388 1273044.4\n",
      "I am working on tract number 2000 of 3446 tracts\n",
      "I am working on tract number 2020 of 3446 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2033 2 258719.22970211325 0.7916\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2033 2 145957.2576354458 0.3958\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2033 2 248432.19331057448 0.7595\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2033 2 206488.6485162248 0.5777\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2033 2 151958.4291017423 0.4867\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2033 2 164505.9542837431 0.5322\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2033 2 184879.5416718363 0.5549\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2033 2 198101.68184624164 0.5663\n",
      "we have 2 non-opposing shorted wedges for tract no 2035\n",
      "I am working on tract number 2040 of 3446 tracts\n",
      "I am working on tract number 2060 of 3446 tracts\n",
      "I am working on tract number 2080 of 3446 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2088\n",
      "we have 2 non-opposing shorted wedges for tract no 2089\n",
      "we have 2 non-opposing shorted wedges for tract no 2090\n",
      "we have 2 non-opposing shorted wedges for tract no 2091\n",
      "we have 2 non-opposing shorted wedges for tract no 2093\n",
      "I am working on tract number 2100 of 3446 tracts\n",
      "I am working on tract number 2120 of 3446 tracts\n",
      "I am working on tract number 2140 of 3446 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2141 0 23862.744361211604 0.4494\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2141 0 381888.55983684305 1.5625\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2141 0 373709.0085444887 1.0059\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2141 0 89137.05379325844 0.7277\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2141 0 346557.3493769368 0.8668\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2141 0 222515.80520241632 0.7972\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2141 0 140147.7970998764 0.7624\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2141 0 179147.42516336768 0.7798\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2141 0 200105.43438227414 0.7885\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2152 5.0 1 41.9 0.1822 219475.3\n",
      "I am working on tract number 2160 of 3446 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2170\n",
      "I am working on tract number 2180 of 3446 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2184\n",
      "I am working on tract number 2200 of 3446 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2207\n",
      "we have 2 non-opposing shorted wedges for tract no 2208\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2214 5.0 0 89.4 1.0854 193428.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2214 6.0 0 89.4 1.076 193428.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2214 7.0 0 89.4 1.0668 193428.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2216 6.0 3 49.4 0.7841 221571.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2216 7.0 3 49.4 0.7656 221571.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2217 2.0 2 90.0 0.7332 236256.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2218 3.0 0 108.6 0.8648 193059.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2218 4.0 0 108.6 0.8586 193059.3\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2219 3 259495.04472322547 0.8146\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2219 3 23431.832405057503 0.4073\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2219 3 260229.01883831396 0.8238\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2219 3 74000.50512087415 0.6156\n",
      "I am working on tract number 2220 of 3446 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2225\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2226 4.0 3 44.7 0.7309 195177.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2227 2.0 2 90.0 0.7598 266482.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2227 5.0 0 83.6 0.7676 259540.3\n",
      "I am working on tract number 2240 of 3446 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2241 5.0 3 49.0 1.1225 248379.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2241 6.0 3 49.0 1.1004 248379.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2241 7.0 3 49.0 1.0787 248379.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2245 8.0 3 53.9 1.2019 312187.3\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2248 3 320742.7277817329 1.2218\n",
      "I am working on tract number 2260 of 3446 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2261 5.0 3 36.4 1.7455 411679.8\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2263 3 447768.6561436828 1.7235\n",
      "we have 2 non-opposing shorted wedges for tract no 2273\n",
      "I am working on tract number 2280 of 3446 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2284 6.0 0 88.7 0.9861 195510.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2284 7.0 0 88.7 0.9698 195510.4\n",
      "I am working on tract number 2300 of 3446 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2315\n",
      "I am working on tract number 2320 of 3446 tracts\n",
      "I am working on tract number 2340 of 3446 tracts\n",
      "I am working on tract number 2360 of 3446 tracts\n",
      "I am working on tract number 2380 of 3446 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2382\n",
      "we have 2 non-opposing shorted wedges for tract no 2396\n",
      "we have 2 non-opposing shorted wedges for tract no 2398\n",
      "we have 2 non-opposing shorted wedges for tract no 2399\n",
      "I am working on tract number 2400 of 3446 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2403\n",
      "I am working on tract number 2420 of 3446 tracts\n",
      "I am working on tract number 2440 of 3446 tracts\n",
      "I am working on tract number 2460 of 3446 tracts\n",
      "I am working on tract number 2480 of 3446 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2487\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2491 2.0 2 90.0 0.4466 1197964.4\n",
      "I am working on tract number 2500 of 3446 tracts\n",
      "I am working on tract number 2520 of 3446 tracts\n",
      "I am working on tract number 2540 of 3446 tracts\n",
      "I am working on tract number 2560 of 3446 tracts\n",
      "I am working on tract number 2580 of 3446 tracts\n",
      "I am working on tract number 2600 of 3446 tracts\n",
      "I am working on tract number 2620 of 3446 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2627 4.0 1 90.0 0.1745 184365.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2627 5.0 1 90.0 0.1793 184365.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2627 6.0 1 90.0 0.1843 184365.9\n",
      "I am working on tract number 2640 of 3446 tracts\n",
      "I am working on tract number 2660 of 3446 tracts\n",
      "I am working on tract number 2680 of 3446 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2681\n",
      "we have 2 non-opposing shorted wedges for tract no 2682\n",
      "we have 2 non-opposing shorted wedges for tract no 2683\n",
      "I am working on tract number 2700 of 3446 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2709 4.0 3 90.0 1.8156 200745.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2709 5.0 3 90.0 1.7502 200745.6\n",
      "I am working on tract number 2720 of 3446 tracts\n",
      "I am working on tract number 2740 of 3446 tracts\n",
      "I am working on tract number 2760 of 3446 tracts\n",
      "I am working on tract number 2780 of 3446 tracts\n",
      "I am working on tract number 2800 of 3446 tracts\n",
      "I am working on tract number 2820 of 3446 tracts\n",
      "I am working on tract number 2840 of 3446 tracts\n",
      "I am working on tract number 2860 of 3446 tracts\n",
      "I am working on tract number 2880 of 3446 tracts\n",
      "I am working on tract number 2900 of 3446 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2906 2.0 0 90.0 0.6454 402765.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2906 3.0 0 90.0 0.3444 402765.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2906 4.0 0 90.0 0.1837 402765.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2908 11.0 0 111.8 0.1373 172544.9\n",
      "I am working on tract number 2920 of 3446 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2937\n",
      "we have 2 non-opposing shorted wedges for tract no 2938\n",
      "we have 2 non-opposing shorted wedges for tract no 2939\n",
      "I am working on tract number 2940 of 3446 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2944\n",
      "I am working on tract number 2960 of 3446 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2977\n",
      "I am working on tract number 2980 of 3446 tracts\n",
      "I am working on tract number 3000 of 3446 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 3004\n",
      "we have 2 non-opposing shorted wedges for tract no 3013\n",
      "I am working on tract number 3020 of 3446 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3023 3 335088.67217370815 1.3632\n",
      "I am working on tract number 3040 of 3446 tracts\n",
      "I am working on tract number 3060 of 3446 tracts\n",
      "I am working on tract number 3080 of 3446 tracts\n",
      "I am working on tract number 3100 of 3446 tracts\n",
      "I am working on tract number 3120 of 3446 tracts\n",
      "I am working on tract number 3140 of 3446 tracts\n",
      "I am working on tract number 3160 of 3446 tracts\n",
      "I am working on tract number 3180 of 3446 tracts\n",
      "I am working on tract number 3200 of 3446 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3203 0 286977.9760375286 1.2805\n",
      "8 yoyos for tract,wedge,wedgePop,r= 3203 0 25551.12304972278 0.6402\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3203 0 288150.19105855527 1.3058\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3203 0 88612.20282580046 0.973\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3203 0 216121.3486788462 1.1394\n",
      "12 yoyos for tract,wedge,wedgePop,r= 3203 0 107339.18581804035 1.0562\n",
      "12 yoyos for tract,wedge,wedgePop,r= 3203 0 146740.7904726959 1.0978\n",
      "12 yoyos for tract,wedge,wedgePop,r= 3203 0 176570.65061646674 1.1186\n",
      "13 yoyos for tract,wedge,wedgePop,r= 3203 0 195269.89668928983 1.129\n",
      "14 yoyos for tract,wedge,wedgePop,r= 3203 0 185575.3676351174 1.1238\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3216 10.0 3 38.7 1.5215 279024.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3216 11.0 3 38.7 1.3671 279024.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3216 16.0 3 38.7 1.5131 279024.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3216 17.0 3 38.7 1.3595 279024.9\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3216 3 279024.9181820279 1.5986\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3217 3 345074.1948000287 1.3818\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3218 3 321599.51027191925 1.373\n",
      "I am working on tract number 3220 of 3446 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3226 6.0 0 90.0 2.7056 312335.1\n",
      "we have 2 non-opposing shorted wedges for tract no 3235\n",
      "I am working on tract number 3240 of 3446 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 3247\n",
      "I am working on tract number 3260 of 3446 tracts\n",
      "I am working on tract number 3280 of 3446 tracts\n",
      "I am working on tract number 3300 of 3446 tracts\n",
      "I am working on tract number 3320 of 3446 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 3328\n",
      "we have 2 non-opposing shorted wedges for tract no 3330\n",
      "I am working on tract number 3340 of 3446 tracts\n",
      "I am working on tract number 3360 of 3446 tracts\n",
      "I am working on tract number 3380 of 3446 tracts\n",
      "I am working on tract number 3400 of 3446 tracts\n",
      "I am working on tract number 3420 of 3446 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 3426\n",
      "we have 2 non-opposing shorted wedges for tract no 3431\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3437 7.0 0 114.5 1.2874 191859.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3437 8.0 0 114.5 1.2842 191859.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3437 9.0 0 114.5 1.2809 191859.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3437 10.0 0 114.5 1.2777 191859.0\n",
      "I am working on tract number 3440 of 3446 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3445 5.0 3 81.4 0.1716 220975.9\n",
      "Sum of weights over all precinct home districts should have been 1.000, but was  1.0000742315490103\n",
      "Average and max number of wedgePop loops per tract were:  6.631456761462565 28.0\n",
      "min,average,max of Home District areas were:  0.0 0.79049770162181 3.9934010440460357\n",
      "calculated statewide vote was 0.4985765553611006, should have been 0.4940024860661418\n",
      "calcd statewide Hispanic pop was 0.0630176825651898, should have been 0.06802035398879688\n",
      "calcd statewide Black pop was 0.09759130212501821, should have been 0.10336118594321483\n",
      "fraction of HDs that with altered wedge angles near boundaries =  0.4051073708647707\n"
     ]
    }
   ],
   "source": [
    "#3/13/22 - from HD2, but using coeff1, coeff2 (unrestricted opp wedge pop, wideAngle quadratic in ratio)\n",
    "# Bisect for yoyo > 3, precinct calcs after wedges finalized.  More aggressive Euler gain\n",
    "#minTractPop = 10  #this is set in a prior block\n",
    "pi=3.1415926536\n",
    "nWedges = 4  #number of wedges per home district polygon  \n",
    "avgWedgeAngle = 2*pi / nWedges\n",
    "wedgeTriAR = [math.sin(pi/nWedges)*math.cos(pi/nWedges)]*nWedges  #wedge triangle area ratio (= area / r*r)\n",
    "angle = [0.]*nWedges\n",
    "angle2 = [0.]*nWedges\n",
    "pt1 = [Point(0,0)]*nWedges  #these four define the polygon wedge for a growing home district\n",
    "pt2 = [Point(0,0)]*nWedges\n",
    "pt3 = [Point(0,0)]*nWedges\n",
    "pt4 = [Point(0,0)]*nWedges\n",
    "oldR = [0.]*nWedges  #wedge radius from previous loop\n",
    "# guessedR = [0.]*nWedges  #obsolete; was wedge radius if we extrapolate from population density of most recent wedge piece\n",
    "printPoly = [Polygon([(0,0),(0,1),(1,1)])]*nWedges #for debugging\n",
    "wedgePop = [0.]*nWedges\n",
    "tractLoopCounter = [0.]* nTracts  #this tracks how many loops per tract to get to target wedge Pop\n",
    "nearEdge = [0.]* nTracts   #not yet implemented; this will flag tracts near map edge for reprocessing\n",
    "tractUse = [0.] * nTracts  #this will store how much we use this tract in ALL HD's vs. expectation\n",
    "precinctUse = [0.] * nPrecincts   #same, but for each precinct / VTD\n",
    "loopTractUse = [0.] * nTracts  #same as above two, but only for the current loop / tract\n",
    "loopPrecinctUse = [0.] * nPrecincts\n",
    "HDvPop = [0.]*nTracts\n",
    "HDvGOP = [0.]*nTracts  #GOP lean of each tract's \"home district\"\n",
    "HDvBlack = [0.]*nTracts\n",
    "HDvHisp = [0.]*nTracts #pct Hispanic by home district\n",
    "HDweight = [0.]*nTracts  #relative weight of each district.  In absence of splits, will equal precinct pop\n",
    "HDarea = [0.]*nTracts  #geographical area of each home district\n",
    "HDradius = [[0.]*nWedges for t in range(nTracts)] #final wedge length for each Home District wedge\n",
    "HDangle = [[0.]*nWedges for t in range(nTracts)] #final included angle for each HD wedge\n",
    "angle0 = [-999.] * nTracts  #orientation of 0th wedge.  Random except re-oriented if we are near-boundary\n",
    "toler = 0.005  #adjustable - fractional slop in district population.  normally 0.01, to be reduced to 0.005\n",
    "tolerPop = toler * avgDistrictPop  #absolute slop in district pop\n",
    "nLoopPrint = 30  #at this loop number, we alert user of problem\n",
    "nGiveUp = 40  #we punt after this many loops\n",
    "nLoopSuperPrint = nGiveUp - 2 # at this loop number, we output wedge growth visuals\n",
    "wrongPop = [0.]*nTracts\n",
    "normalGain = 0.8  #adjustable - a bit less than 1.0 for stability, how far we step relative to expected perfect guess\n",
    "EulerGain = 1.5 #if we hit empty land, gain more aggressively to get through it\n",
    "#yoyoFactor = 0.3 #rate of reduction in gain as solver continues to yoyo in solving a wedge  #not currently used ***\n",
    "globalMax_dr = (MAP.area/nDistricts) ** 0.5  #put a reasonable upper limit on wedge radial growth step size\n",
    "tractPrintInterval = 20  #for tracking progress\n",
    "minTractArea = 0.00001 * MAP.area / nTracts  #failsafe for later div-by-zero\n",
    "homePopDensity = avgGridDensity  #seed this\n",
    "didWeRestart = [0] * nTracts  #will flag if we adjusted wedge angles due to a single shorted wedge\n",
    "coeff1 = 0.3  #linear term. we will adjust this empirically to tighten tract weighting near boundaries\n",
    "coeff2 = 0.3  #quadratic term.  These two control distortion in wedge angles relative to wedgePop shortness\n",
    "minWedgePop = [avgDistrictPop/nWedges] * nTracts  #wedge pop for wedge facing boundary (for tracts near boundaries)\n",
    "maxWideAngle = 0.8 * pi  #adjustable parameter.  (avoid wedges too close to half a pie\n",
    "\n",
    "for t in range(nTracts) :  #(nTracts):  #loop on each tract.  Start by resetting stats\n",
    "    gain = normalGain  #in case last precinct had convergence problems\n",
    "    if (t % tractPrintInterval) == 0 : \n",
    "        print(\"I am working on tract number {0} of {1} tracts\".format(t,nTracts) )\n",
    "    tractLoopCounter[t] = 0.  #for stability stats, will track how many times we need to loop for each tract\n",
    "    # isActiveG = [0]*nGrids  #resets whether each grid is relevant to this tract (not used; see alt grid turn-on method)\n",
    "    if (tractArea[t] > minTractArea) :  #too lazy to indent everything.  We'll catch zero tractArea later\n",
    "        homePopDensity = tractPop[t]/tractArea[t]  #temporary - estimate the local density for initial step\n",
    "    tractCP = Point(tractGeom[t].centroid.x,tractGeom[t].centroid.y)  #shorthand for centroid of each tract\n",
    "    for nG in range(nGrids):\n",
    "        if gridGeom[nG].contains(tractCP) :  #which grid contains the centroid of this tract?  Turn it on!\n",
    "            # isActiveG[nG] = 1  #I don't think I use this\n",
    "            homeGridDensity = gridDensity[nG]\n",
    "    maxStartDensity = max(homePopDensity,homeGridDensity)\n",
    "    minR = (avgDistrictPop / maxStartDensity / pi)**0.5   # conservative estimated radius of this tract's district\n",
    "    tinyR = 0.01 * minR   #ensures we start with small, but not infinitesimal wedge populations\n",
    "    angle0[t] = random.uniform(0,2.*pi)  #imparts random orientation to our starting wedge\n",
    "    loopTractUse = [0.]*nTracts  #this and below help us reset tract n precinct use after a wedge reset\n",
    "    loopPrecinctUse = [0.]*nPrecincts\n",
    "    # ***GROW WEDGES SIMULTANEOUSLY via ARRAYS, SO WE CAN REACT ON FLY TO BOUNDARY STOPS\n",
    "    HDpop = 0.  #running total of this tract's \"home district\" population\n",
    "    nActiveWedges = nWedges #active wedges have not run over boudnary\n",
    "    targetWedgePop = [avgDistrictPop / nWedges]*nWedges  #at beginning, split districtPop equally among wedges\n",
    "    #latestWedgeDensity = [0.]*nWedges  #stop using # pop'n density of the wedge piece we just added or subtracted\n",
    "    wedgePop = [0.]*nWedges\n",
    "    oldWedgePop = [0.]*nWedges  #wedge pop from previous round (needed for NR projection)\n",
    "    wedgePopGap = [0.]*nWedges\n",
    "    isOverEdge = [0] *nWedges #each wedge is still fully inside map boundary\n",
    "    isSatisfied = [0] * nWedges #wedges close enough to target are not improved upon\n",
    "    yoyoCount = [0] * nWedges #tracks how many times we've reversed this wedge's growth vs. shrink at current wPop target\n",
    "    wedgeMaxR = [0.] * nWedges #will track the largest radius this wedge has seen (to cap yoyo bisection step)\n",
    "    wedgeAngle = [avgWedgeAngle] * nWedges #reset to equi-angle when starting each tract    \n",
    "    currentR = [0.]*nWedges\n",
    "    old_dr = [0.]*nWedges\n",
    "    wedgeStop = 0  #obsolete? this will flag if we need to rejigger oldR when we stopped an over-boundary wedge\n",
    "    for nW in range(nWedges):\n",
    "        currentR[nW] = tinyR  #each wedge starts as a tiny triangle of this radius\n",
    "        old_dr[nW] = currentR[nW] #this will track the last radial step (to help convergence)\n",
    "    oldR = [0.]*nWedges   #for remembering last loop's wedge radius\n",
    "    max_dr = [globalMax_dr]*nWedges  #reset max possible positive radial step size\n",
    "    for nW in range(nWedges) :  #this loop: set up tiny wedges in each direction to seed each wedge loop\n",
    "        angle[nW] = (nW-0.5)*wedgeAngle[nW]+angle0[t]   #local variable for orientation of START of wedge\n",
    "        angle2[nW] = angle[nW]+wedgeAngle[nW]      #local angle for clockwise END of wedge\n",
    "        pt1[nW] = tractCP\n",
    "        pt2[nW] = Point(tractCP.x+currentR[nW]*math.cos(angle[nW]),  tractCP.y+currentR[nW]*math.sin(angle[nW]) )\n",
    "        pt3[nW] = Point(tractCP.x+currentR[nW]*math.cos(angle2[nW]), tractCP.y+currentR[nW]*math.sin(angle2[nW]) )\n",
    "        wedgePoly = Polygon([pt1[nW], pt2[nW], pt3[nW] ]) #build a tiny starter wedge triangle\n",
    "        wedgePop[nW] = tractPop[t]* wedgePoly.area/max(tractArea[t],minTractArea)  #max prevents rare div-by-zero\n",
    "    HDpop = np.sum(wedgePop)\n",
    "    if (tractPop[t] < minTractPop or tractArea[t] == 0 or isSkippedTract[t] == 1):\n",
    "        #go directly to jail, do not pass Go, this tract doesn't count\n",
    "        HDpop = avgDistrictPop  #white lie to kick us out of loop\n",
    "    while abs (HDpop - avgDistrictPop) > tolerPop :  #until we've grown the home district to the right size...\n",
    "        sumWedgePopGapChange = 0  #this will total wedge pops that fall short of expectations (when over boundary)\n",
    "        for nW in range(nWedges) :  #for each wedge, we'll build to gain pop or shrink to lose population ...\n",
    "            neededWedgePop = targetWedgePop[nW] - wedgePop[nW]\n",
    "            isSatisfied[nW] = 0  #default in case target changed last loop.  We'll check immediately below\n",
    "            if abs(neededWedgePop/targetWedgePop[nW]) < 0.5*toler :  #is this wedge close enough to stop iterating on it?\n",
    "                isSatisfied[nW] = 1\n",
    "            if isOverEdge[nW] == 0 and isSatisfied[nW] == 0:   #we skip over-boundary and near-perfect wedges\n",
    "                wedgePopDelta = wedgePop[nW] - oldWedgePop[nW]  #how much wedgePop gained in last loop\n",
    "                if (wedgePopDelta == 0): #our last wedge change was in an empty area (desert or off map)\n",
    "                    gainAdjust = EulerGain/gain  #Euler method often over-cautious at map edges or in sparse areas\n",
    "                    guessedRsquared = currentR[nW]*currentR[nW] * targetWedgePop[nW] / wedgePop[nW]  #Euler guess\n",
    "                    # can't use Newton-Raphson since last dy was zero, use Euler instead\n",
    "                    print(\"we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop\",t,tractLoopCounter[t],\n",
    "                          nW, round(180./pi*wedgeAngle[nW],1),round(currentR[nW],4),round(wedgePop[nW],1))\n",
    "                else: #use N-R estimation\n",
    "                    gainAdjust = 1.0\n",
    "                    R2delta = currentR[nW]*currentR[nW] - oldR[nW]*oldR[nW]  #this and below are run/rise of current slope\n",
    "                    guessedRsquared = currentR[nW]*currentR[nW] + R2delta / wedgePopDelta * neededWedgePop\n",
    "                    \n",
    "                guessedRsquared = max( guessedRsquared, 0. ) #minimum guardrail\n",
    "                guessed_dr = guessedRsquared ** 0.5 - currentR[nW]  #best guess for wedge radius change\n",
    "                #gain = max(0.3,normalGain ** (1. + yoyoFactor * yoyoCount[nW]))  #obsolete; using bisect\n",
    "                dr = gain * gainAdjust * guessed_dr   #gain typically ~0.8 for stability\n",
    "                dr = max( -0.5*currentR[nW],min(dr,max_dr[nW]) )  #apply min and max guardrails to wedge radius change\n",
    "                \n",
    "                if np.sign(dr) != np.sign(old_dr[nW]) :  #are we yoyoing? How many times?\n",
    "                    yoyoCount[nW] += 1\n",
    "                if yoyoCount[nW] > 3 and yoyoCount[nW] <= 6 :\n",
    "                    wedgeMaxR[nW] = max(wedgeMaxR[nW],currentR[nW]) #will track largest R in yoyo cycle\n",
    "                    max_dr[nW] = 2.*wedgeMaxR[nW]  #this will now store a reasonable max bisection step size\n",
    "                if yoyoCount[nW] > 6 :  #switch to bisection method; too many yo-yo's\n",
    "                    print(yoyoCount[nW],\"yoyos for tract,wedge,wedgePop,r=\",t,nW,wedgePop[nW],round(currentR[nW],4) )\n",
    "                    max_dr[nW] = 0.5 * max_dr[nW] #we are now bisecting ...\n",
    "                    if(neededWedgePop < 0):\n",
    "                        dr = max(-1.*max_dr[nW], -0.5*currentR[nW])  #reduce wedge size\n",
    "                    else:\n",
    "                        dr = max_dr[nW]  #increase wedge size\n",
    "                old_dr[nW] = dr  #these three lines -- save current as old values for next loop\n",
    "                oldR[nW] = currentR[nW]\n",
    "                oldWedgePop[nW] = wedgePop[nW]\n",
    "                if dr > 0. :  #we are growing a wedge trapezoid piece \n",
    "                    outerR = currentR[nW] + dr\n",
    "                    innerR = currentR[nW]\n",
    "                    currentR[nW] = outerR    #for next loop around\n",
    "                else:    #this wedge trapezoid piece will be SUBTRACTED from current wedge\n",
    "                    outerR = currentR[nW]\n",
    "                    innerR = currentR[nW] + dr  #remember, dr is negative here\n",
    "                    currentR[nW] = innerR             #for next loop around\n",
    "                #now, describe the new wedge to probe for precinct intersections ...\n",
    "                pt1[nW] = Point(tractCP.x+innerR*math.cos(angle[nW]), tractCP.y+innerR*math.sin(angle[nW]) )\n",
    "                pt2[nW] = Point(tractCP.x+outerR*math.cos(angle[nW]), tractCP.y+outerR*math.sin(angle[nW]) )\n",
    "                pt3[nW] = Point(tractCP.x+outerR*math.cos(angle2[nW]),tractCP.y+outerR*math.sin(angle2[nW]) )\n",
    "                pt4[nW] = Point(tractCP.x+innerR*math.cos(angle2[nW]),tractCP.y+innerR*math.sin(angle2[nW]) )\n",
    "                wedgePoly = Polygon([pt1[nW], pt2[nW], pt3[nW],pt4[nW]])  #true for wedge add-on or to-be-trimmed\n",
    "                \n",
    "                printPoly[nW] = wedgePoly  #for debugging\n",
    "                latestWedgePop = 0.  #for the new piece, not the entire triangle\n",
    "                usedTract = [0]*nTracts  #rezero lists of tracts and precincts that could straddle multiple grids\n",
    "                usedPrecinct = [0]*nPrecincts\n",
    "                for nG in range(nGrids) :  # loop over ACTIVE grids to look for intersecting tracts\n",
    "                    gridIntersxn = gridGeom[nG].intersection(wedgePoly)\n",
    "                    if (gridIntersxn.area > 0) :  #this grid is RELEVANT to this wedge\n",
    "                        for tt in range(nGridTracts[nG]) : #look for intersxns with all tracts in this grid\n",
    "                            nTT = gridTractNo[nG][tt] #shorthand for this tract's global tract no\n",
    "                            if(usedTract[nTT] == 0) :  #Did we not already look at this tract in another grid list?\n",
    "                                usedTract[nTT] = 1  #well, now we have!  Probe intersection with wedge\n",
    "                                overlap = tractGeom[nTT].intersection(wedgePoly).area\n",
    "                                if overlap > 0 :\n",
    "                                    fracArea = overlap/tractArea[nTT]\n",
    "                                    latestWedgePop  += fracArea*tractPop[nTT]  #always positive (used in density calc)\n",
    "                                    loopTractUse[nTT] += np.sign(dr)*overlap/tractArea[nTT] * tractPop[t]/avgDistrictPop\n",
    "                        # found all possible tract overlaps with this increment / decrement to this wedge.\n",
    "                        \n",
    "                wedgePop[nW] += np.sign(dr)*latestWedgePop  #for full triangle, based on this latest piece\n",
    "                # Now, flag if we're beyond boundary\n",
    "                if wedgePop[nW] < targetWedgePop[nW] : #if growing, confirm we're not beyond MAP boundary\n",
    "                    leadingEdge = LineString([pt2[nW],pt3[nW]])\n",
    "                    if leadingEdge.intersects(MAP) :  #still room to grow in part of edge, keep going\n",
    "                        gerrymandering = \"evil\"  #it had to be said\n",
    "                    else:  #this wedge is fully beyond the map. give up on more map intersection\n",
    "                        isOverEdge[nW] = 1\n",
    "                        shortedWedge = nW  #ID'ing highest numbered wedge that got shorted by the boundary in this loop\n",
    "                        oldWedgePopGap = wedgePopGap[nW]\n",
    "                        wedgePopGap[nW] = targetWedgePop[nW] - wedgePop[nW]  #how far this wedge's pop is below its target\n",
    "                        nActiveWedges -= 1  #a few lines below, we will adjust other wedge's targets\n",
    "                        sumWedgePopGapChange += wedgePopGap[nW] - oldWedgePopGap\n",
    "                        if (nActiveWedges == 0) : #we're doomed, somehow all wedges are short and off map\n",
    "                            print(\"PUNT! no more active wedges for tract, loop =\",t,tractLoopCounter[t] )\n",
    "                            tractLoopCounter[t] = nGiveUp + 1  #PUNT !!!\n",
    "                            \n",
    "                        max_dr = [globalMax_dr]*nWedges   #allow other wedges to take big steps again to catch up\n",
    "                        yoyoCount = [0] * nWedges  #go back to original gains on ALL active wedges\n",
    "                   \n",
    "        # end of nW loop to adjust all wedge populations by growing or trimming wedges, IDing over-edge wedges\n",
    "        tractLoopCounter[t] +=1   # still looping on home district Pop.\n",
    "        nReceivingWedges = nActiveWedges\n",
    "        oppFlag = 0\n",
    "        if 0 == 1: #always false; for HD2 we would have checked here for opp wedge restriction; ignore this block\n",
    "            # targetWedgePop[int(nWedges/2)] < 0.99 * avgDistrictPop/float(nWedges): #is opp wedge restricted?\n",
    "            oppFlag = 1\n",
    "            nReceivingWedges -= 1-isOverEdge[int(nWedges/2)]  #if yes, don't count opposite wedge as a receiver\n",
    "        if nReceivingWedges == 0: #rarely, the opposite wedge is the only active one\n",
    "            for nWW in range(nWedges): #in this case, we allow the opposite wedge to pick up the slack\n",
    "                targetWedgePop[nWW] += sumWedgePopGapChange/max(1.,float(nReceivingWedges) )\n",
    "        else: #if the opp wedge has a ceiling wedgePopTarget, distribute wedgePopGap to other active wedges\n",
    "            for nWW in range(nWedges):  #time to make adjustments in target wedge pops based on boundary fails\n",
    "                if nWW != int(nWedges/2) or oppFlag == 0 :  #excluding flagged opposite wedge as a receiver\n",
    "                    targetWedgePop[nWW] += sumWedgePopGapChange/float(nReceivingWedges)\n",
    "\n",
    "        # *** NEW 1/19/22 CODE TO ADJUST WEDGE ANGLES WHEN A BOUNDARY ENCOUNTERED\n",
    "        if (nActiveWedges == nWedges or nActiveWedges < nWedges - 2\n",
    "           or didWeRestart[t] == 1) :  #0 or 3+ over-boundary short wedges, or we've already adjusted wedge angles once\n",
    "            HDpop = np.sum(wedgePop) #Keep going with normal wedge growth and trim process\n",
    "        else :  # 1 OR 2 SHORTED WEDGES.  MAY WANT TO ALTER WEDGE ANGLES\n",
    "            didWeRestart[t] = 1  #to ensure we adjust wedge angles and target wedgePops at most once per tract\n",
    "            if (nActiveWedges == nWedges - 2) :  #exactly two wedges got shorted in this loop -- are they opposing?\n",
    "                oppW = int( (shortedWedge + nWedges/2) % nWedges)  #the index of wedge opposite the last shorted wedge\n",
    "                if (isOverEdge[oppW] == 0) :\n",
    "                    print(\"we have 2 non-opposing shorted wedges for tract no\",t)\n",
    "                    totalLiveAngle = 2.*pi - np.sum(isOverEdge)*avgWedgeAngle  #avail angle to divvy among live wedges  \n",
    "                    for nW in range (nWedges) : #Decrease wedge angles opposite shorted wedges via a complex weighting\n",
    "                        if isOverEdge[nW] == 0 :\n",
    "                            oppW = int( (nW + nWedges/2) % nWedges) \n",
    "                            angleWeight = (np.sum(wedgePopGap)-wedgePopGap[oppW])/np.sum(wedgePopGap)/(nActiveWedges-1)\n",
    "                            wedgeAngle[nW] = angleWeight * totalLiveAngle  \n",
    "                else : #shorted wedges ARE opposing\n",
    "                    print(\"we have 2 OPPOSING shorted wedges for tract no\",t) # no wedge-angle adjustment in this case\n",
    "                    #wedgeAngle=[avgWedgeAngle]*nWedges  #comment this out, no change\n",
    "            else: # a single shorted wedge (over boundary).  COMPLETELY RESTART wedge growth process\n",
    "                shortW = shortedWedge #Next dozen lines: convoluted code to find angle to closest boundary point\n",
    "                #print(\"1 wedge over map boundary for tract\",t,\"at loop, wedgePop\",tractLoopCounter[t],wedgePop[shortW] )\n",
    "                shortedPoly = Polygon([tractCP, pt2[shortW],pt3[shortW] ])\n",
    "                MAPedge = shortedPoly.intersection(MAP.exterior) #true state boundary line where wedge crossed it\n",
    "                minDistance = max(tractCP.distance(MAPedge),tinyR)  #closest distance to edge where wedge hit the boundary\n",
    "                edgeCircle = tractCP.buffer(1.01*minDistance)  #build a circle a little bigger than this distance\n",
    "                #  1.001 is not big enough; will occasionally not intersect\n",
    "                closeArea = edgeCircle.intersection(MAPedge)\n",
    "                counter = 0.\n",
    "                maxCounter = 10. #give up after 10 tries\n",
    "                while closeArea.is_empty :  # protect against missing the map boundary somehow\n",
    "                    print(\"need to widen edge circle beyond\",minDistance, \"for tract (x,y)=(\",tractCP.x,tractCP.y)\n",
    "                    minDistance = minDistance * 1.1\n",
    "                    edgeCircle = tractCP.buffer(minDistance)  #widen the circle\n",
    "                    closeArea = edgeCircle.intersection(MAPedge)\n",
    "                    counter += 1\n",
    "                    if counter >= maxCounter: #not finding map edge intersection.  Try another way\n",
    "                        print(\"did not find boundary with circle approach.  Brute-force it.\")\n",
    "                        minDistance = tractCP.distance(MAP.exterior)\n",
    "                        edgeCircle = tractCP.buffer(1.01*minDistance)\n",
    "                        closeArea = edgeCircle.intersection(MAP.exterior)                        \n",
    "            \n",
    "                closePoint = closeArea.centroid  # this is a point very close to the closest beeline from tract to map edge\n",
    "                #print(closePoint,tractCP, \"are boundary point and tract center point\")\n",
    "                x1 = closePoint.x\n",
    "                x2 = tractCP.x  #debugging\n",
    "                dx = closePoint.x - tractCP.x\n",
    "                dy = closePoint.y - tractCP.y\n",
    "                exitAngle = pi/2. * np.sign(dy)  #default in case dx=0\n",
    "                if (dx != 0 ):\n",
    "                    exitAngle = math.atan(dy/dx)  #this is the angle from the x-axis to the exit beeline\n",
    "                    if dx < 0 :  #use complementary atan solution; boundary is west of tract centroid\n",
    "                        exitAngle = pi + exitAngle\n",
    "                angle0[t] = exitAngle # this reorients 0th wedge to face boundary's closest point               \n",
    "                # New 1/23/22 - let's estimate wedgePopGap at ideal orientation, normal angle   # ****************\n",
    "                wedgeAngle1 = exitAngle - 0.5*avgWedgeAngle\n",
    "                wedgeAngle2 = exitAngle + 0.5*avgWedgeAngle\n",
    "                maxR = max(stateWidth,stateHeight) #ensuring we make a big enough wedge\n",
    "                wedgePt1 = tractCP\n",
    "                wedgePt2 = Point(tractCP.x+maxR*math.cos(wedgeAngle1),  tractCP.y+maxR*math.sin(wedgeAngle1) )\n",
    "                wedgePt3 = Point(tractCP.x+maxR*math.cos(wedgeAngle2), tractCP.y+maxR*math.sin(wedgeAngle2) )\n",
    "                wedgePoly = Polygon([ wedgePt1, wedgePt2, wedgePt3 ])\n",
    "                minWedgePop[t] = 0. #the wedgePop of the shorted wedge if we re-orient but don't adjust angles\n",
    "                usedTract = [0]*nTracts  #rezero lists of tracts and precincts that could straddle multiple grids\n",
    "                for nG in range(nGrids) :  # loop over ACTIVE grids to look for intersecting tracts\n",
    "                    gridIntersxn = gridGeom[nG].intersection(wedgePoly)\n",
    "                    if (gridIntersxn.area > 0) :  #this grid is RELEVANT to this wedge\n",
    "                        for tt in range(nGridTracts[nG]) : #look for intersxns with all tracts in this grid\n",
    "                            nTT = gridTractNo[nG][tt] #shorthand for this tract's global tract no\n",
    "                            if(usedTract[nTT] == 0) :  #Did we not already look at this tract in another grid list?\n",
    "                                usedTract[nTT] = 1  #well, now we have!  Probe intersection with wedge\n",
    "                                overlap = tractGeom[nTT].intersection(wedgePoly).area\n",
    "                                if overlap > 0 :\n",
    "                                    fracArea = overlap/tractArea[nTT]\n",
    "                                    minWedgePop[t]  += fracArea*tractPop[nTT]  #always positive (used in density calc)\n",
    "                #print(\"the minimum shorted Wedge Pop for tract {0} is {1}\".format(t,minWedgePop[t]) )  #debug\n",
    "                #printAngle[t] = round(exitAngle*180./pi,4) #for debugging\n",
    "                printDist = round(minDistance,4)\n",
    "                #print(t,\"(\",tractCP.x,tractCP.y,\")\",printAngle,printDist,\"t,tract(x,y),exitAngle,dist\")\n",
    "                # ********************************************************************\n",
    "                # assign new wedge angles.  The two wide angles face toward and away from nearest boundary\n",
    "                #ratio = wedgePopGap[shortW]/ (avgDistrictPop/nWedges)  #0-1; this is degree of shortness\n",
    "                ratio = 1. - minWedgePop[t]/ (avgDistrictPop/nWedges)  # *** UPDATED\n",
    "                # printWedgePopGap[t] = wedgePopGap[shortW]\n",
    "                wideAngle = (1. + coeff1 * ratio + coeff2 * ratio*ratio)*avgWedgeAngle #HERE, WE ADJUST ANGLES\n",
    "                wideAngle = min(wideAngle, maxWideAngle*pi) #set an upper limit\n",
    "                thinAngle = (2.*pi - 2.*wideAngle)/(nWedges - 2.)  #other wedges equally angle-compressed\n",
    "                #if (t % 9 == 0):  #occasional print\n",
    "                    #print(\"tract,wide, thin angles are \",t,round(180/pi*wideAngle,4),round(180/pi*thinAngle,4) )\n",
    "                for nW in range(nWedges) :\n",
    "                    if (nW == 0 or nW == int(nWedges/2) ) : #assign these two as shorted wedge and its opposite\n",
    "                        wedgeAngle[nW] = wideAngle\n",
    "                    else :\n",
    "                        wedgeAngle[nW] = thinAngle\n",
    "                #** COMPLETELY RESTART WEDGE GROWTH PROCESS FROM INITIAL PIZZA SLICES, NOW RE-ORIENTED\n",
    "                loopTractUse = [0.] * nTracts  # reset as we are starting over\n",
    "                loopPrecinctUse = [0.]* nPrecincts\n",
    "                HDpop = 0.\n",
    "                nActiveWedges = nWedges\n",
    "                targetWedgePop = [avgDistrictPop/nWedges] * nWedges\n",
    "                oldR = [0.]*nWedges   #for remembering last loop's wedge radius  \n",
    "                currentR = [tinyR]*nWedges                \n",
    "                old_dr = [tinyR]*nWedges  #this will track the last radial step (to help convergence)\n",
    "                max_dr = [globalMax_dr]*nWedges  #reset max step size\n",
    "                wedgePop = [0.]*nWedges\n",
    "                oldWedgePop = [0.]*nWedges #wedge pop from previous round\n",
    "                wedgePopGap = [0.]*nWedges\n",
    "                isOverEdge = [0] *nWedges\n",
    "                isSatisfied = [0] *nWedges\n",
    "                yoyoCount = [0] *nWedges #just in case we missed this earlier\n",
    "                wedgeMaxR = [0.] * nWedges #resetting\n",
    "                angle[0] = angle0[t] - 0.5*wideAngle  #the 0th wedge always faces the boundary\n",
    "                angle2[0] = angle[0] + wedgeAngle[0]\n",
    "                for nW in range(nWedges) :\n",
    "                    if (nW == 0):\n",
    "                        angle[nW] = angle0[t] - 0.5*wideAngle  #we have to start somewhere :-)\n",
    "                    else :\n",
    "                        angle[nW] = angle2[nW-1]\n",
    "                    angle2[nW] = angle[nW]+wedgeAngle[nW]      \n",
    "                    pt1[nW] = tractCP\n",
    "                    pt2[nW] = Point(tractCP.x+tinyR*math.cos(angle[nW]),  tractCP.y+tinyR*math.sin(angle[nW]) )\n",
    "                    pt3[nW] = Point(tractCP.x+tinyR*math.cos(angle2[nW]), tractCP.y+tinyR*math.sin(angle2[nW]) )\n",
    "                    wedgePoly = Polygon([pt1[nW], pt2[nW], pt3[nW] ])\n",
    "                    wedgePop[nW] = tractPop[t]* wedgePoly.area/max(tractArea[t],minTractArea)\n",
    "                HDpop = np.sum(wedgePop)   #** END OF RESTART BLOCK.  RE-ENTER MAIN LOOP FOR THIS TRACT\n",
    "                      \n",
    "        # **** BELOW IS TO CORRECT FOR NONCONVERGENCE *****\n",
    "        if (tractLoopCounter[t] > nLoopPrint):  #may be becoming unstable. Alert user,    #and (don't) reduce gain\n",
    "            strPop = str(round(HDpop,2))\n",
    "            strWdg = \"Overedge?\"\n",
    "            strSat = \"Satisfied?\"\n",
    "            strYoyo = \"yoyo?\"                \n",
    "            strTWP_dr = \"tWP,dr\"\n",
    "            for nWW in range(nWedges) :\n",
    "                strPop = strPop + \", \"+str(round(wedgePop[nWW],1) )\n",
    "                strWdg = strWdg +str(isOverEdge[nWW])+\", \"\n",
    "                strSat = strSat +str(isSatisfied[nWW])\n",
    "                strYoyo = strYoyo + str(yoyoCount[nWW])\n",
    "                strTWP_dr = strTWP_dr + \", \"+str(round(targetWedgePop[nWW],1) )+\",\"+str(round(old_dr[nWW],4) )\n",
    "            print(\"loop{0}, tr{1},wedgePops{2}, {3},{4},{5} \".format(tractLoopCounter[t],t,strPop,strWdg,strSat,strYoyo) )\n",
    "            print(\"   targetWP, latest drx4 are\",strTWP_dr)\n",
    "\n",
    "        if (tractLoopCounter[t] > nLoopSuperPrint) :\n",
    "            for nWW in range(nWedges):                \n",
    "                print(\"wedge no, currentR,oldWedgePop, wedgePop, overEdge?\",nWW,str(round(currentR[nWW],5)), \n",
    "                      str(round(oldWedgePop[nWW],4)), str(round(wedgePop[nWW],4)),isOverEdge[nWW])   #debug\n",
    "                x, y = printPoly[nWW].exterior.xy     #wedge debugging .....\n",
    "                plt.plot(x, y, c=(0.1, 0.2, 0.02+float(nWW)/nWedges) )\n",
    "            plt.show()\n",
    "        if(tractLoopCounter[t] >= nGiveUp):\n",
    "            print(\"I looped {0} times on tract {1}, giving up w pop {2}\".format(nGiveUp,t,HDpop) )\n",
    "            wrongPop[t] = HDpop  #this will flag this HD as triaged\n",
    "            HDpop = avgDistrictPop  #white lie to kick out of loop\n",
    "        # *** END OF TRIAGE FOR NONCONVERGENCE\n",
    "        \n",
    "    # END OF WHILE LOOP --> we are within tolerance of avgDistrictPop. Finalize this tract's Home District stats\n",
    "    for nTT in range (nTracts) :\n",
    "        tractUse[nTT] += loopTractUse[nTT]\n",
    "    totGOP = 0.\n",
    "    totVote = 0.\n",
    "    totVAP = 0.\n",
    "    totHisp = 0.\n",
    "    totBlack = 0.  #zero these out prior to summing over final wedges\n",
    "    usedTract = [0]*nTracts  #rezero lists of tracts and precincts that could straddle multiple grids\n",
    "    usedPrecinct = [0]*nPrecincts\n",
    "\n",
    "    if (tractPop[t] < minTractPop or tractArea[t] == 0 or isSkippedTract[t] == 1): \n",
    "        #we bypassed the big loop; this tract is inconsequential\n",
    "        HDvPop[t] = HDpop  #a white lie\n",
    "        HDweight[t] = 0.000001  #to suppress this in total stats\n",
    "    else :\n",
    "        HDvPop[t] = np.sum(wedgePop)\n",
    "        # HDvHisp[t] = np.sum(wedgeHisp)/np.sum(wedgePop)  #3/2/22 - move to final wedge calcs\n",
    "        # HDvBlack[t] = np.sum(wedgeBlack)/np.sum(wedgePop)  #3/2/22 - move to final wedge calcs\n",
    "        HDweight[t] = tractPop[t]/np.sum(tractPop)\n",
    "        nearEdge[t] = nWedges - nActiveWedges  #flagging the number of wedges that were not completely pop-filled\n",
    "        centerPt = tractCP\n",
    "        outerPt2 = Point(tractCP.x+currentR[0]*math.cos(angle[0]), tractCP.y+currentR[0]*math.sin(angle[0]) )\n",
    "        outerPt3 = Point(tractCP.x+currentR[0]*math.cos(angle2[0]),tractCP.y+currentR[0]*math.sin(angle2[0]) )\n",
    "        HDpolly = Polygon([centerPt,outerPt2,outerPt3])   #initiate a polygon that will be the home district\n",
    "        for nW in range(nWedges):\n",
    "            HDradius[t][nW] = currentR[nW]\n",
    "            HDangle[t][nW] = angle[nW]\n",
    "            \n",
    "        for nW in range(1,nWedges):  #save final geometry, compute minority and partisan stats\n",
    "            cR = currentR[nW] #shorthand\n",
    "            outerPt2 = Point(tractCP.x+cR*math.cos(angle[nW]), tractCP.y+cR*math.sin(angle[nW]) )\n",
    "            outerPt3 = Point(tractCP.x+cR*math.cos(angle2[nW]),tractCP.y+cR*math.sin(angle2[nW]) )\n",
    "            HDpolly =  HDpolly.union( Polygon([centerPt,outerPt2,outerPt3]) )  #add this wedge to HD district polygon\n",
    "        HDpolly = HDpolly.intersection(MAP)  #exclude map's convex hull and buffer, just the original union of precincts\n",
    "        HDarea[t] = HDpolly.area  #for stats, final Home District area\n",
    "        for nG in range(nGrids) :  # ID the ACTIVE grids to look for intersecting precincts\n",
    "            gridIntersxn = gridGeom[nG].intersection(HDpolly)\n",
    "            if (gridIntersxn.area > 0) :  #this grid is RELEVANT to the final Home District\n",
    "                for tt in range(nGridTracts[nG]):\n",
    "                    nTT = gridTractNo[nG][tt]\n",
    "                    if usedTract[nTT] == 0: #only examine tracts that have not already been called for intersection\n",
    "                        usedTract[nTT] == 1  #this tract has now been called \n",
    "                        overlap = tractGeom[nTT].intersection(HDpolly).area\n",
    "                        if overlap > 0 :\n",
    "                            fracArea = overlap/tractArea[nTT]\n",
    "                            totVAP += fracArea*tractVAP[nTT]\n",
    "                            totHisp += fracArea*tractHisp[nTT]\n",
    "                            totBlack += fracArea*tractBlack[nTT]\n",
    "                        \n",
    "                for pp in range(nGridPrecincts[nG]): #scanning the list of precincts in this active grid\n",
    "                    nPP = gridPrecinctNo[nG][pp]\n",
    "                    if usedPrecinct[nPP] == 0  :\n",
    "                        usedPrecinct[nPP] = 1  #don't double up on precinct intersection\n",
    "                        overlap = vtdGeom[nPP].intersection(HDpolly).area\n",
    "                        if overlap > 0 :\n",
    "                            fracArea = overlap/vtdArea[nPP]\n",
    "                            totGOP += fracArea*vtdGOP[nPP]*vtdPop[nPP]\n",
    "                            totVote += fracArea*vtdPop[nPP]\n",
    "                            loopPrecinctUse[nPP] += overlap/vtdArea[nPP] *tractPop[t]/avgDistrictPop\n",
    "        HDvHisp[t] = totHisp/totVAP\n",
    "        HDvBlack[t] = totBlack/totVAP\n",
    "        HDvGOP[t] = totGOP / totVote\n",
    "        for nPP in range (nPrecincts) :\n",
    "            precinctUse[nPP] += loopPrecinctUse[nPP] #add to global use for this precinct\n",
    "            \n",
    "        \n",
    "\n",
    "    # end of loop on this tract\n",
    "for t in range(nTracts):\n",
    "    if(wrongPop[t] > 0):\n",
    "        HDvPop[t] = wrongPop[t]  #undo the lie that got us out of the loop early\n",
    "HDsumWeight = np.sum(HDweight)\n",
    "print(\"Sum of weights over all precinct home districts should have been 1.000, but was \",HDsumWeight)\n",
    "print(\"Average and max number of wedgePop loops per tract were: \",np.average(tractLoopCounter),np.max(tractLoopCounter) )\n",
    "print(\"min,average,max of Home District areas were: \",np.min(HDarea),np.average(HDarea),np.max(HDarea) )\n",
    "stateGOP2 = 0.\n",
    "stateHisp2 = 0.\n",
    "stateBlack2 = 0.\n",
    "for t in range(nTracts):\n",
    "    HDweight[t] = HDweight[t]/HDsumWeight   #renormalizing\n",
    "    stateGOP2 += HDweight[t]*HDvGOP[t]\n",
    "    stateBlack2 += HDweight[t]*HDvBlack[t]\n",
    "    stateHisp2 += HDweight[t]*HDvHisp[t]\n",
    "statePop = np.sum(tractPop)\n",
    "print(\"calculated statewide vote was {0}, should have been {1}\".format(stateGOP2, stateGOP) )\n",
    "print(\"calcd statewide Hispanic pop was {0}, should have been {1}\".format(stateHisp2, np.sum(tractHisp)/stateVAP ) )\n",
    "print(\"calcd statewide Black pop was {0}, should have been {1}\".format(stateBlack2, np.sum(tractBlack)/stateVAP ) )\n",
    "print(\"fraction of HDs that with altered wedge angles near boundaries = \",np.sum(didWeRestart)/nTracts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "24a347a9-8220-4c1a-bcfc-14923f1f98f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sum of weights over all precinct home districs should have been 1.000, but was  1.0004902766193386\n",
      "Average and max number of wedgePop loops per tract were:  5.784260848247119 40.0\n",
      "min,average,max of Home District areas were:  0.0 0.18129500977866808 0.7724780914809968 for  MD\n"
     ]
    }
   ],
   "source": [
    "#use this line to document progress\n",
    "# start PA HD1 10:00, end 11:42\n",
    "print(\"Sum of weights over all precinct home districs should have been 1.000, but was \",HDsumWeight)\n",
    "print(\"Average and max number of wedgePop loops per tract were: \",np.average(tractLoopCounter),np.max(tractLoopCounter) )\n",
    "print(\"min,average,max of Home District areas were: \",np.min(HDarea),np.average(HDarea),np.max(HDarea),\"for \",STATE )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "72247942-899a-4ab2-9796-239aa1e524fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PA 17Mar\n"
     ]
    }
   ],
   "source": [
    "date = \"17Mar\"\n",
    "print(STATE, date)  #check that I will not overwrite existing file :-)\n",
    "tractCPx = [0.]*nTracts\n",
    "tractCPy = [0.]*nTracts\n",
    "tractNo = [0.]*nTracts\n",
    "for t in range(nTracts):\n",
    "    tractCPx[t]=tractGeom[t].centroid.x\n",
    "    tractCPy[t]=tractGeom[t].centroid.y\n",
    "    tractNo[t]=t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "c55ac5fd-7402-47b9-9093-10b9fe0912b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#LET'S WRITE AN OUTPUT FILE BEFORE WE FORGET :-)\n",
    "#convert HD wedge geometries to 1D arrays.  BELOW IS FOR 4-WEDGE.  CAN AUGMENT FOR 6-WEDGE\n",
    "HDangle0 = [0.]*nTracts\n",
    "HDradius0 = [0.]*nTracts\n",
    "HDangle1 = [0.]*nTracts\n",
    "HDradius1 = [0.]*nTracts\n",
    "HDangle2 = [0.]*nTracts\n",
    "HDradius2 = [0.]*nTracts\n",
    "HDangle3 = [0.]*nTracts\n",
    "HDradius3 = [0.]*nTracts\n",
    "for t in range(nTracts):\n",
    "    HDangle0[t] = HDangle[t][0]\n",
    "    HDradius0[t] = HDradius[t][0]\n",
    "    HDangle1[t] = HDangle[t][1]\n",
    "    HDradius1[t] = HDradius[t][1]\n",
    "    HDangle2[t] = HDangle[t][2]\n",
    "    HDradius2[t] = HDradius[t][2]\n",
    "    HDangle3[t] = HDangle[t][3]\n",
    "    HDradius3[t] = HDradius[t][3]\n",
    "# now convert output to pandas dataframe and export\n",
    "\n",
    "paramList = [\"STATE\",\"stateGOP\",\"nDistricts\",\"nTracts\",\"nPrecincts\",\"nWedges\",\"popn-toler\",\n",
    "             \"gain\",\"coeff1\",\"coeff2\"]\n",
    "paramValues = [STATE,stateGOP, nDistricts, nTracts,nPrecincts,nWedges, toler, gain, coeff1,coeff2]\n",
    "for i in range(nTracts-len(paramList)):\n",
    "    paramList.append(\".\")\n",
    "    paramValues.append(-99)  #so all columns have same number of entries, even the parameter list\n",
    "df = pd.DataFrame( {\"paramList\": paramList,\"paramValues\":paramValues,\"HD-pop\":HDvPop,\"HDvGOP\":HDvGOP,\"HDvHisp\":HDvHisp,\n",
    "                    \"HDvBlack\":HDvBlack,\"HDwt\":HDweight,\"HDarea\":HDarea, \"HDangle0\":HDangle0, \"HDangle1\":HDangle1,\n",
    "                    \"HDangle2\":HDangle2,\"HDangle3\":HDangle3,\"HDradius0\":HDradius0,\"HDradius1\":HDradius1,\n",
    "                    \"HDradius2\":HDradius2,\"HDradius3\":HDradius3,\"startAngle\":angle0,\"tractNo\":tractNo,\n",
    "                    \"Loops\":tractLoopCounter,\"tractPop\":tractPop,\"tractHisp\":tractHisp,\"tractBlack\":tractBlack,\n",
    "                    \"centroid x\":tractCPx,\"centroid y\":tractCPy, \"tractUse\":tractUse,\"nearEdge\":nearEdge,\n",
    "                    \"wrongPop\":wrongPop,\"Restart?\":didWeRestart} ) \n",
    "\n",
    "outname = STATE+str(nDistricts)+\"HD1tol\"+str(toler)+\"nW\"+str(nWedges)+date+\".csv\"\n",
    "outpath = \"state_HD_output/\"+outname\n",
    "df.to_csv(outpath)\n",
    "precinctNo = [0.]*nPrecincts\n",
    "vtdX = [0.]*nPrecincts\n",
    "vtdY = [0.]*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    precinctNo[p]=p\n",
    "    vtdX[p] = vtdGeom[p].centroid.x\n",
    "    vtdY[p] = vtdGeom[p].centroid.y\n",
    "df2 = pd.DataFrame( {\"precinctNo\":precinctNo,\"precinctPop\":vtdPop,\"precUse\":precinctUse, \"vtdX\":vtdX, \"vtdY\":vtdY} )\n",
    "outname2 = STATE+date+\"_VTD_tol\"+str(toler)+\"nW\"+str(nWedges)+\".csv\"\n",
    "outpath = \"state_HD_output/\"+outname2\n",
    "df2.to_csv(outpath)  #currently, I'm outputting the precinct use stats to a separate file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "c4449da0-47a9-40cc-a585-8288447290e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we will redo tract numbers  [0]\n"
     ]
    }
   ],
   "source": [
    "# Looking for nonconvergers ...  Run even if none observed, just to check  \n",
    "redo = [0]\n",
    "counter = 0.\n",
    "for t in range (nTracts) :\n",
    "    if wrongPop[t] > 0 :\n",
    "        ratio = round(wrongPop[t]/avgDistrictPop,2)\n",
    "        tractX = tractGeom[t].centroid.x\n",
    "        tractY = tractGeom[t].centroid.y        \n",
    "        print(t, ratio,\"(\",tractX,tractY,\")\",HDvGOP[t], \"t, pop/target,(x,y), pctR\")\n",
    "        if (ratio < 0.9 or ratio > 1.1) :  #these we will redo\n",
    "            plt.text(tractGeom[t].centroid.x, tractGeom[t].centroid.y,ratio, fontsize=14)\n",
    "            if counter == 0 :  #first redo\n",
    "                redo = [t]\n",
    "            else :\n",
    "                redo.append(t)\n",
    "            counter+= 1\n",
    "        else: #show these milder offenders on the map in a smaller font            \n",
    "            plt.text(tractGeom[t].centroid.x, tractGeom[t].centroid.y,ratio, fontsize=9)\n",
    "\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"purple\")\n",
    "plt.show()\n",
    "print(\"we will redo tract numbers \",redo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "be6e965b-4ca8-4af2-8031-63132fbb939d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "754 0.7960709103276775 1097 pop,GOP 0.5068368277119416\n",
      "760 0.7998521662215651 1393 pop,GOP 0.4723618090452261\n",
      "767 0.7792672879885789 793 pop,GOP 0.2244640605296343\n",
      "771 0.7883688266807388 331 pop,GOP 0.2175226586102719\n",
      "780 0.787056446478989 835 pop,GOP 0.1724550898203593\n",
      "783 0.7812717785050259 593 pop,GOP 0.2209106239460371\n",
      "795 0.7878064262179683 551 pop,GOP 0.24863883847549909\n",
      "796 0.7846294980217218 427 pop,GOP 0.2903981264637002\n",
      "797 0.7833325362313266 491 pop,GOP 0.24439918533604887\n",
      "798 0.7771348889395447 634 pop,GOP 0.3028391167192429\n",
      "799 0.7790755123683867 506 pop,GOP 0.24308300395256918\n",
      "800 0.7717377541411963 538 pop,GOP 0.22304832713754646\n",
      "808 0.7908082240372449 747 pop,GOP 0.2918340026773762\n",
      "809 0.7863268319352695 803 pop,GOP 0.3150684931506849\n",
      "810 0.7726663022555696 485 pop,GOP 0.27628865979381445\n",
      "845 0.7760841659690181 865 pop,GOP 0.3352601156069364\n",
      "846 0.7623048761682997 1071 pop,GOP 0.36134453781512604\n",
      "848 0.7816972814972546 1473 pop,GOP 0.45010183299389\n",
      "849 0.7703936713095277 1756 pop,GOP 0.4043280182232346\n",
      "850 0.7851882625218124 1005 pop,GOP 0.4716417910447761\n",
      "851 0.7821287251286667 914 pop,GOP 0.4989059080962801\n",
      "852 0.7996967351771782 1718 pop,GOP 0.5034924330616997\n",
      "918 0.766071923902497 1055 pop,GOP 0.4312796208530806\n",
      "920 0.7722610411883788 923 pop,GOP 0.39869989165763814\n",
      "990 0.7787418529099459 5839 pop,GOP 0.4622366843637609\n",
      "1006 0.7284821988720847 6263 pop,GOP 0.4349353345042312\n",
      "1009 0.6442336105060383 894 pop,GOP 0.37919463087248323\n",
      "1015 0.655039028625935 871 pop,GOP 0.36280137772675086\n",
      "1020 0.7563712646453905 6473 pop,GOP 0.3933261238992739\n",
      "1024 0.7995423085208305 5799 pop,GOP 0.43645456113122955\n",
      "1040 0.7414540995723178 5712 pop,GOP 0.44345238095238093\n",
      "1255 0.7677348388002387 788 pop,GOP 0.26903553299492383\n",
      "1256 0.7975157682788008 1143 pop,GOP 0.5765529308836396\n",
      "1260 0.7909221461034778 792 pop,GOP 0.7247474747474747\n",
      "1263 0.767625408949579 292 pop,GOP 0.6952054794520548\n",
      "1267 0.7603599183901298 2115 pop,GOP 0.43451536643026006\n",
      "1284 0.7944745756391415 1103 pop,GOP 0.2810516772438803\n",
      "1286 0.7972767513540227 794 pop,GOP 0.25692695214105793\n",
      "1293 0.7767364670992148 664 pop,GOP 0.15813253012048192\n",
      "1294 0.7720486742460978 522 pop,GOP 0.16091954022988506\n",
      "1300 0.7787788676783393 471 pop,GOP 0.7537154989384289\n",
      "1649 0.7996208736644905 508 pop,GOP 0.41732283464566927\n",
      "1650 0.7976322507957125 455 pop,GOP 0.46153846153846156\n",
      "1652 0.7684413659446307 1855 pop,GOP 0.48194070080862533\n",
      "1654 0.7832053966868101 1383 pop,GOP 0.4945770065075922\n",
      "1657 0.7815909484449433 780 pop,GOP 0.5384615384615384\n",
      "1658 0.7715739349067438 1276 pop,GOP 0.5532915360501567\n",
      "1707 0.7956436019839572 694 pop,GOP 0.21037463976945245\n",
      "1708 0.7583777420426518 234 pop,GOP 0.19658119658119658\n",
      "1709 0.7890919990060172 961 pop,GOP 0.10509885535900104\n",
      "1710 0.7661249807112818 613 pop,GOP 0.13050570962479607\n",
      "1711 0.7782486388422957 791 pop,GOP 0.05941845764854614\n",
      "1712 0.7484989951780854 393 pop,GOP 0.178117048346056\n",
      "1713 0.7659877426489561 497 pop,GOP 0.15090543259557343\n",
      "1714 0.7981832709649487 1001 pop,GOP 0.12287712287712288\n",
      "1715 0.7347122146203087 535 pop,GOP 0.19065420560747665\n",
      "1716 0.7494070174688405 670 pop,GOP 0.20298507462686566\n",
      "1717 0.7645352486123892 1096 pop,GOP 0.15145985401459855\n",
      "1718 0.7786074599830604 1008 pop,GOP 0.1636904761904762\n",
      "1719 0.7458417167950837 369 pop,GOP 0.15447154471544716\n",
      "1720 0.7856768607257033 615 pop,GOP 0.13821138211382114\n",
      "1721 0.7820903559687373 592 pop,GOP 0.1875\n",
      "1722 0.7863536383053017 563 pop,GOP 0.19715808170515098\n",
      "1723 0.7825150841014074 700 pop,GOP 0.07142857142857142\n",
      "1725 0.7806057108672404 844 pop,GOP 0.17772511848341233\n",
      "1766 0.7810673744108664 308 pop,GOP 0.3051948051948052\n",
      "1777 0.7993865507468968 1628 pop,GOP 0.2493857493857494\n",
      "1783 0.799220035122245 311 pop,GOP 0.3247588424437299\n",
      "1785 0.7890192271886766 358 pop,GOP 0.2541899441340782\n",
      "1786 0.7883579562188666 380 pop,GOP 0.32105263157894737\n",
      "1792 0.7878385062508317 1701 pop,GOP 0.2328042328042328\n",
      "1834 0.7050591930079222 734 pop,GOP 0.4564032697547684\n",
      "1841 0.5985902338155722 449 pop,GOP 0.28507795100222716\n",
      "1842 0.6312713234376907 200 pop,GOP 0.305\n",
      "1843 0.6318275401103036 246 pop,GOP 0.3089430894308943\n",
      "1844 0.7451794000364262 1169 pop,GOP 0.397775876817793\n",
      "1845 0.5902468232226975 674 pop,GOP 0.2818991097922849\n",
      "1846 0.6136736854122726 647 pop,GOP 0.3323029366306028\n",
      "1847 0.59059597688746 707 pop,GOP 0.2857142857142857\n",
      "1848 0.6206744311581729 699 pop,GOP 0.4248927038626609\n",
      "1849 0.6013981414482547 495 pop,GOP 0.27070707070707073\n",
      "1850 0.6147871580337011 240 pop,GOP 0.2708333333333333\n",
      "1851 0.6310428760537172 278 pop,GOP 0.08633093525179857\n",
      "1852 0.6798679161489767 498 pop,GOP 0.357429718875502\n",
      "1853 0.702216819781747 693 pop,GOP 0.3463203463203463\n",
      "1854 0.6087053271975814 438 pop,GOP 0.3378995433789954\n",
      "1855 0.636940293775072 296 pop,GOP 0.17229729729729729\n",
      "1856 0.7056631161423228 1452 pop,GOP 0.4366391184573003\n",
      "1857 0.6120981523343133 146 pop,GOP 0.2328767123287671\n",
      "1858 0.6405893457103731 510 pop,GOP 0.16470588235294117\n",
      "1859 0.6531071358674264 589 pop,GOP 0.3225806451612903\n",
      "1860 0.6510861559015471 700 pop,GOP 0.23714285714285716\n",
      "1861 0.6257545484681594 356 pop,GOP 0.3089887640449438\n",
      "1862 0.6191793400727998 332 pop,GOP 0.2891566265060241\n",
      "1863 0.6299317037183825 408 pop,GOP 0.30637254901960786\n",
      "1864 0.6251259103660418 512 pop,GOP 0.33984375\n",
      "1865 0.6285274571410034 503 pop,GOP 0.27634194831013914\n",
      "1866 0.663796836043881 564 pop,GOP 0.3829787234042553\n",
      "1867 0.6239109446727553 1067 pop,GOP 0.42361761949390814\n",
      "1868 0.6570497228627835 298 pop,GOP 0.33557046979865773\n",
      "1869 0.6661976462124782 1246 pop,GOP 0.36998394863563405\n",
      "1870 0.712649497333158 524 pop,GOP 0.45610687022900764\n",
      "1871 0.6184840022613287 562 pop,GOP 0.2277580071174377\n",
      "1872 0.6261466014640027 287 pop,GOP 0.2264808362369338\n",
      "1873 0.6512148761552525 1047 pop,GOP 0.3648519579751671\n",
      "1874 0.6418621451188335 437 pop,GOP 0.21052631578947367\n",
      "1875 0.6641988122612464 811 pop,GOP 0.32305795314426633\n",
      "1876 0.6656556641806024 708 pop,GOP 0.3898305084745763\n",
      "1877 0.662007939663028 561 pop,GOP 0.24598930481283424\n",
      "1878 0.6516312451300158 356 pop,GOP 0.23876404494382023\n",
      "1879 0.643731482483508 282 pop,GOP 0.19858156028368795\n",
      "1880 0.6893932756534399 669 pop,GOP 0.32884902840059793\n",
      "1881 0.7273294171652759 842 pop,GOP 0.42874109263657956\n",
      "1882 0.6876239292615889 465 pop,GOP 0.4064516129032258\n",
      "1883 0.7333310203093623 629 pop,GOP 0.38950715421303655\n",
      "1884 0.6746643199415033 1363 pop,GOP 0.3947175348495965\n",
      "1885 0.7171114563046326 1385 pop,GOP 0.43032490974729243\n",
      "1886 0.690007102932215 523 pop,GOP 0.4225621414913958\n",
      "1887 0.725047883718762 693 pop,GOP 0.37662337662337664\n",
      "1888 0.6551941514913099 511 pop,GOP 0.3542074363992172\n",
      "1889 0.6766611350037626 439 pop,GOP 0.36446469248291574\n",
      "1890 0.7085711268732765 601 pop,GOP 0.36605657237936773\n",
      "1891 0.65779997648538 416 pop,GOP 0.3894230769230769\n",
      "1892 0.7445200754253655 1208 pop,GOP 0.4056291390728477\n",
      "1893 0.7508552050530632 870 pop,GOP 0.41724137931034483\n",
      "1894 0.6833185719975783 862 pop,GOP 0.38979118329466356\n",
      "1896 0.7945070961499945 1229 pop,GOP 0.515052888527258\n",
      "1898 0.7629098259432904 709 pop,GOP 0.5684062059238364\n",
      "1900 0.787315496659929 1632 pop,GOP 0.5520833333333334\n",
      "1901 0.7642327409649051 1038 pop,GOP 0.7090558766859345\n",
      "1902 0.6330740512125975 1489 pop,GOP 0.48085963734049697\n",
      "1903 0.6467430371799481 1096 pop,GOP 0.5675182481751825\n",
      "1904 0.6769213330931465 1406 pop,GOP 0.5732574679943101\n",
      "1906 0.6883382765072257 695 pop,GOP 0.4115107913669065\n",
      "1907 0.7489950333780755 1718 pop,GOP 0.529685681024447\n",
      "1908 0.7403915281420312 1081 pop,GOP 0.43108233117483813\n",
      "1909 0.7643651301654614 1407 pop,GOP 0.47121535181236673\n",
      "1910 0.774360351140222 977 pop,GOP 0.49948822927328557\n",
      "1911 0.7902592482626939 852 pop,GOP 0.42018779342723006\n",
      "1913 0.6823884335401559 906 pop,GOP 0.45916114790286977\n",
      "1914 0.7131862504500684 673 pop,GOP 0.49925705794947994\n",
      "1915 0.7259949647770361 964 pop,GOP 0.47717842323651455\n",
      "1916 0.789192652394276 1213 pop,GOP 0.47320692497938993\n",
      "1917 0.7911863563586852 1382 pop,GOP 0.4840810419681621\n",
      "1921 0.7667668504638113 1465 pop,GOP 0.5296928327645051\n",
      "1923 0.7611355320658363 1678 pop,GOP 0.5172824791418356\n",
      "1926 0.6373114802228723 1334 pop,GOP 0.46101949025487254\n",
      "1927 0.7161844602476415 1090 pop,GOP 0.5045871559633027\n",
      "1928 0.669006164360354 1880 pop,GOP 0.5074468085106383\n",
      "1929 0.7102124292278776 979 pop,GOP 0.5383043922369765\n",
      "1930 0.7582974173905622 1422 pop,GOP 0.5682137834036568\n",
      "1932 0.6805156639112248 942 pop,GOP 0.6072186836518046\n",
      "1933 0.6327403784934007 570 pop,GOP 0.41754385964912283\n",
      "1934 0.7494360354963389 1500 pop,GOP 0.5206666666666667\n",
      "1935 0.7486912043116488 816 pop,GOP 0.48651960784313725\n",
      "1938 0.7930216579565694 838 pop,GOP 0.45704057279236276\n",
      "1939 0.6773390585010681 1002 pop,GOP 0.590818363273453\n",
      "1942 0.762974489409066 1631 pop,GOP 0.6351931330472103\n",
      "1951 0.6646735243871138 743 pop,GOP 0.5020188425302826\n",
      "1952 0.6631952443283008 694 pop,GOP 0.521613832853026\n",
      "1957 0.7361036899158213 1132 pop,GOP 0.7473498233215548\n",
      "1977 0.6837150886546682 430 pop,GOP 0.7697674418604651\n",
      "1979 0.7587012754246351 1587 pop,GOP 0.6427221172022685\n",
      "2040 0.7540691219750113 242 pop,GOP 0.8099173553719008\n",
      "2042 0.7472142128432783 131 pop,GOP 0.6717557251908397\n",
      "2045 0.753249497286817 135 pop,GOP 0.7777777777777778\n",
      "2047 0.7796617575097676 134 pop,GOP 0.7089552238805971\n",
      "2050 0.7648145960660522 264 pop,GOP 0.7121212121212122\n",
      "2054 0.7938359314289429 411 pop,GOP 0.7883211678832117\n",
      "2057 0.7793276007081114 227 pop,GOP 0.7709251101321586\n",
      "2059 0.7497458583816051 167 pop,GOP 0.7724550898203593\n",
      "2205 0.7911027075252224 208 pop,GOP 0.47115384615384615\n",
      "2206 0.7929029824279014 1153 pop,GOP 0.6131830008673027\n",
      "2207 0.7493238637744014 157 pop,GOP 0.643312101910828\n",
      "2208 0.7261138828367983 275 pop,GOP 0.5890909090909091\n",
      "2209 0.7471012645290944 217 pop,GOP 0.4700460829493088\n",
      "2219 0.7127619465674323 140 pop,GOP 0.5357142857142857\n",
      "2220 0.7291417540209821 429 pop,GOP 0.5664335664335665\n",
      "2224 0.748336253067414 1433 pop,GOP 0.6127006280530356\n",
      "2225 0.7556897551799573 285 pop,GOP 0.5333333333333333\n",
      "2241 0.76174268762048 1674 pop,GOP 0.6027479091995221\n",
      "2271 0.706278440205678 394 pop,GOP 0.5228426395939086\n",
      "2967 0.7855573450497081 3768 pop,GOP 0.3760615711252654\n",
      "5038 0.6965640344846115 1991 pop,GOP 0.6454043194374686\n",
      "5039 0.7105025404870642 1970 pop,GOP 0.666497461928934\n",
      "5040 0.7074395105138597 3611 pop,GOP 0.6114649681528662\n",
      "5041 0.76241971878128 3328 pop,GOP 0.6328125\n",
      "5042 0.7824002824897204 1609 pop,GOP 0.6594157862026103\n",
      "5044 0.6932043275915807 977 pop,GOP 0.5885363357215967\n",
      "5046 0.7214706677848765 2494 pop,GOP 0.44065757818765033\n",
      "5047 0.773654479421163 2468 pop,GOP 0.3841166936790924\n",
      "5048 0.6429726621411117 627 pop,GOP 0.4864433811802233\n",
      "5050 0.799710916894417 272 pop,GOP 0.6029411764705882\n",
      "5051 0.7431567816366523 1519 pop,GOP 0.6142198815009875\n",
      "5052 0.687327817794317 1543 pop,GOP 0.6325340246273493\n",
      "5275 0.7182327502986965 293 pop,GOP 0.6962457337883959\n",
      "5279 0.7877648947276067 285 pop,GOP 0.5964912280701754\n",
      "5281 0.7327149270177318 1238 pop,GOP 0.6688206785137318\n",
      "5282 0.7115247692200011 724 pop,GOP 0.6146408839779005\n",
      "5285 0.7348767714782365 630 pop,GOP 0.7603174603174603\n",
      "5288 0.7700900712167383 921 pop,GOP 0.7274701411509229\n",
      "5290 0.7079831769629459 133 pop,GOP 0.7593984962406015\n",
      "5291 0.7007378649938231 108 pop,GOP 0.5740740740740741\n",
      "5292 0.7910575547743598 528 pop,GOP 0.6685606060606061\n",
      "5293 0.7894472194854273 760 pop,GOP 0.7539473684210526\n",
      "5294 0.7141482453455974 485 pop,GOP 0.5670103092783505\n",
      "5296 0.7010874604297113 286 pop,GOP 0.7622377622377622\n",
      "5297 0.7280927083793887 506 pop,GOP 0.7806324110671937\n",
      "5300 0.7921106467783798 1050 pop,GOP 0.7476190476190476\n",
      "5304 0.7805391420177681 997 pop,GOP 0.7883650952858575\n",
      "5305 0.7106156294959026 252 pop,GOP 0.6547619047619048\n",
      "5307 0.77296090418023 358 pop,GOP 0.6871508379888268\n",
      "5308 0.7244864298144162 230 pop,GOP 0.6391304347826087\n",
      "5309 0.7108624678645442 255 pop,GOP 0.7254901960784313\n",
      "5459 0.7785717934161461 729 pop,GOP 0.7421124828532236\n",
      "5460 0.7973927209508962 772 pop,GOP 0.772020725388601\n",
      "5461 0.7444936905591185 168 pop,GOP 0.6011904761904762\n",
      "5462 0.7113550659322029 303 pop,GOP 0.636963696369637\n",
      "5463 0.7535128681571999 528 pop,GOP 0.7083333333333334\n",
      "5466 0.7163729238423878 765 pop,GOP 0.7032679738562092\n",
      "5467 0.7176957514903803 462 pop,GOP 0.6406926406926406\n",
      "5468 0.731684294246807 1290 pop,GOP 0.6643410852713179\n",
      "5469 0.7069896013770847 256 pop,GOP 0.7734375\n",
      "5470 0.6982322921305871 888 pop,GOP 0.6768018018018018\n",
      "5473 0.6842478082169887 473 pop,GOP 0.7463002114164905\n",
      "5475 0.7707095765393434 445 pop,GOP 0.6853932584269663\n",
      "5476 0.7296271174697804 858 pop,GOP 0.6363636363636364\n",
      "5478 0.7840973390337169 615 pop,GOP 0.5788617886178862\n",
      "5480 0.6937754146191637 411 pop,GOP 0.805352798053528\n",
      "5482 0.7122407455951887 868 pop,GOP 0.6705069124423964\n",
      "5483 0.7463305004742501 454 pop,GOP 0.7400881057268722\n",
      "5484 0.7105527143707103 608 pop,GOP 0.6792763157894737\n",
      "5485 0.7406386558251632 136 pop,GOP 0.7352941176470589\n",
      "5486 0.7967860958338263 1035 pop,GOP 0.7314009661835749\n",
      "5488 0.700291580518684 99 pop,GOP 0.6868686868686869\n",
      "5489 0.746159934040878 637 pop,GOP 0.7017268445839875\n",
      "5682 0.7757252959124907 752 pop,GOP 0.5452127659574468\n",
      "5683 0.7760699977028134 1087 pop,GOP 0.5354185832566697\n",
      "5693 0.6720086914356997 529 pop,GOP 0.5708884688090737\n",
      "5694 0.6638173239068923 700 pop,GOP 0.55\n",
      "5702 0.799383867023108 788 pop,GOP 0.5038071065989848\n",
      "5703 0.7032775702415183 204 pop,GOP 0.6764705882352942\n",
      "5704 0.7084401908004666 572 pop,GOP 0.583916083916084\n",
      "5819 0.7604246223151689 757 pop,GOP 0.2800528401585205\n",
      "5820 0.7680010462805233 1341 pop,GOP 0.24906785980611484\n",
      "5821 0.7524045514800666 1323 pop,GOP 0.25245653817082386\n",
      "5822 0.7670972333018201 1671 pop,GOP 0.28545780969479356\n",
      "5823 0.7797738377744807 919 pop,GOP 0.3220892274211099\n",
      "5824 0.7939580610389001 814 pop,GOP 0.3476658476658477\n",
      "5827 0.7611420121460671 1914 pop,GOP 0.3176593521421108\n",
      "5879 0.6755928854567411 1630 pop,GOP 0.6441717791411042\n",
      "5880 0.69549250211663 1922 pop,GOP 0.6451612903225806\n",
      "5881 0.7824564763271306 1773 pop,GOP 0.43090806542583193\n",
      "5882 0.698471725231998 580 pop,GOP 0.3931034482758621\n",
      "5883 0.6812756809319221 623 pop,GOP 0.33386837881219905\n",
      "5884 0.6049296376358925 305 pop,GOP 0.3475409836065574\n",
      "5885 0.6161087349798967 416 pop,GOP 0.1875\n",
      "5888 0.6330045822510986 733 pop,GOP 0.48021828103683495\n",
      "5891 0.7535167898626187 747 pop,GOP 0.3949129852744311\n",
      "5892 0.713635387907982 655 pop,GOP 0.39083969465648855\n",
      "5893 0.6249291017958385 306 pop,GOP 0.30718954248366015\n",
      "5894 0.625468659038789 583 pop,GOP 0.3653516295025729\n",
      "5906 0.7786003608715953 453 pop,GOP 0.7505518763796909\n",
      "5907 0.7951274364580077 239 pop,GOP 0.7280334728033473\n",
      "6045 0.758286299962385 554 pop,GOP 0.11371841155234658\n",
      "6046 0.7693678560945241 1011 pop,GOP 0.08605341246290801\n",
      "6047 0.7773373641461119 404 pop,GOP 0.11386138613861387\n",
      "6048 0.775643828287809 813 pop,GOP 0.09348093480934809\n",
      "6281 0.7881643976209051 1822 pop,GOP 0.4264544456641054\n",
      "6282 0.7831870175210072 860 pop,GOP 0.42209302325581394\n",
      "6283 0.7669567010875905 643 pop,GOP 0.5318818040435459\n",
      "6284 0.7901176623179164 1138 pop,GOP 0.45254833040421794\n",
      "6286 0.7988245791344171 883 pop,GOP 0.3827859569648924\n",
      "6290 0.7989063443547351 1254 pop,GOP 0.5366826156299841\n",
      "6294 0.7919939069690074 913 pop,GOP 0.5279299014238773\n",
      "6523 0.3868878998222041 1894 pop,GOP 0.4910242872228089\n",
      "6525 0.573740592210227 5732 pop,GOP 0.4305652477320307\n",
      "6526 9.733750326181031e-17 5468 pop,GOP 0.4347110460863204\n",
      "6527 0.7271764862626323 7179 pop,GOP 0.43864047917537263\n",
      "6528 0.6605012745707712 2151 pop,GOP 0.39562993956299397\n",
      "6536 0.7596739695226514 1475 pop,GOP 0.43050847457627117\n",
      "6545 0.7646377231588554 5613 pop,GOP 0.4379119900231605\n",
      "6546 0.7800082406974602 5560 pop,GOP 0.4422661870503597\n",
      "6550 0.7765976226782008 5291 pop,GOP 0.41372141372141374\n",
      "6551 0.7899997639591513 1709 pop,GOP 0.47922761849034523\n",
      "6559 0.7084635979223171 2430 pop,GOP 0.47901234567901235\n",
      "6560 0.7738554127511139 6195 pop,GOP 0.45924132364810333\n",
      "6561 0.7379883522121292 1912 pop,GOP 0.5277196652719666\n",
      "6565 8.336662996543929e-17 1690 pop,GOP 0.38698224852071006\n",
      "6570 0.7284627216410231 1515 pop,GOP 0.4752475247524752\n",
      "6571 0.7398799080668257 1464 pop,GOP 0.5088797814207651\n",
      "6640 0.7396685668676803 201 pop,GOP 0.4527363184079602\n",
      "6641 0.7385054570304568 727 pop,GOP 0.48005502063273725\n",
      "6642 0.7370697954694863 325 pop,GOP 0.5538461538461539\n",
      "6643 0.7340294986766831 181 pop,GOP 0.5303867403314917\n",
      "6644 0.7378060717943814 309 pop,GOP 0.5242718446601942\n",
      "6645 0.737021495809804 301 pop,GOP 0.5016611295681063\n",
      "6646 0.7344466239896265 382 pop,GOP 0.450261780104712\n",
      "6647 0.7451654042524186 603 pop,GOP 0.5008291873963516\n",
      "6648 0.7432393524625758 475 pop,GOP 0.5494736842105263\n",
      "6649 0.7400856083833801 410 pop,GOP 0.5365853658536586\n",
      "6650 0.7508999189675707 302 pop,GOP 0.5761589403973509\n",
      "6651 0.7601190397389119 451 pop,GOP 0.5676274944567627\n",
      "6652 0.7560547149339419 271 pop,GOP 0.5756457564575646\n",
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      "6811 0.7959758865616375 636 pop,GOP 0.3742138364779874\n",
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      "8323 0.6319864134206815 730 pop,GOP 0.4547945205479452\n",
      "8324 0.7922182730705161 1415 pop,GOP 0.4657243816254417\n",
      "8325 0.6962322259329407 720 pop,GOP 0.7458333333333333\n",
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      "8409 0.7591189156516104 424 pop,GOP 0.5424528301886793\n",
      "8410 0.767292763717574 446 pop,GOP 0.5134529147982063\n",
      "8463 0.7887937962165492 2266 pop,GOP 0.27140335392762577\n",
      "8466 0.7748303949564807 1034 pop,GOP 0.25338491295938104\n",
      "8467 0.7928527491270502 1572 pop,GOP 0.35368956743002544\n",
      "8468 0.7901553060845949 1410 pop,GOP 0.36028368794326243\n",
      "8733 0.611796910414163 1205 pop,GOP 0.49294605809128633\n",
      "8919 0.74136688539795 462 pop,GOP 0.6623376623376623\n",
      "8920 0.7793487667189514 1385 pop,GOP 0.6469314079422382\n",
      "8922 0.7238770457281475 862 pop,GOP 0.7621809744779582\n",
      "8923 0.7158956777917824 1171 pop,GOP 0.7480785653287788\n",
      "8924 0.6955438340617407 842 pop,GOP 0.6923990498812351\n",
      "8925 0.7172881342304376 1099 pop,GOP 0.732484076433121\n",
      "8932 0.7878362451260545 656 pop,GOP 0.5274390243902439\n",
      "8933 0.7916545284706686 2841 pop,GOP 0.5976768743400211\n",
      "8934 0.7651139513189728 1503 pop,GOP 0.6094477711244178\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting underused PRECINCTS\n",
    "for p in range (nPrecincts) :\n",
    "    if precinctUse[p] < 0.8 and isSkippedPrecinct[p] == 0 and vtdPop[p] > 0: #ignore suppressed precincts\n",
    "        print(p,precinctUse[p], vtdPop[p],\"pop,GOP\",vtdGOP[p])\n",
    "        pU = round(precinctUse[p],3)\n",
    "        tractX = vtdGeom[p].centroid.x\n",
    "        tractY = vtdGeom[p].centroid.y        \n",
    "        # print(t, ratio,\"(\",tractX,tractY,\")\",HDvGOP[t], \"t, pop/target,(x,y), pctR\")\n",
    "        plt.text(vtdGeom[p].centroid.x, vtdGeom[p].centroid.y,pU, fontsize=9)\n",
    "        if notPolyVTD[p] == 0 :\n",
    "            x,y = vtdGeom[p].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "        else :\n",
    "            for geom in vtdGeom[p].geoms:\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y)\n",
    "\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"purple\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "id": "37f47a77-d58e-4b4c-be60-676819128e21",
   "metadata": {},
   "outputs": [],
   "source": [
    "# (OPTIONAL) IF WE STOPPED AND RESTARTED, read the data back in:  #NEED TO UPDATE THIS W/ANGLES, RADII\n",
    "import pandas as pd\n",
    "infilename = \"FL_nD28tol0.01nW4.csv\"\n",
    "df2 = pd.read_csv(\"state_HD_output/\"+infilename)\n",
    "HDvPop = df2[\"HD-pop\"]\n",
    "HDvHisp = df2[\"HDvHisp\"]\n",
    "HDvGOP = df2['HDvGOP']\n",
    "HDweight = df2['HDwt']\n",
    "HDarea = df2['HDarea']\n",
    "tractLoopCounter = df2['Loops']\n",
    "tractPop = df2['tractPop']\n",
    "tractCPx = df2['centroid x']\n",
    "tractCPy = df2['centroid y']\n",
    "tractUse = df2['tractUse']\n",
    "# nearEdge = df2['nearEdge']   #add this back in, PLEASE\n",
    "df2.head()\n",
    "nTracts = len(tractPop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e9b2c0c0-fb53-44e5-9155-1963d4833ee8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "750596.7418101031 0.1131774380110593 0.0001361943772 0.0400616118781349 0.7450137754374908\n"
     ]
    }
   ],
   "source": [
    "#  HERE'S A BLOCK TO OVERWRITE THE REDONE DATA\n",
    "for r in range(nRedos):\n",
    "    t = redo[r]\n",
    "    HDvPop[t] = redoHDvPop[r]\n",
    "    HDvGOP[t] = redoHDvGOP[r]\n",
    "    tractArea[t] = redoHDarea[r]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c80e0880-6811-4408-b847-6d70bd2546ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt   #SKIP THIS BLOCK IF STARTING FROM FIRST BLOCK\n",
    "from scipy.stats import norm\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "c8f73bb9-e36b-41c7-9220-1ec09169aa3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# all done with redo's.  Let's look at some outputs.  First, red/blue by home district  SHOULD BE MIX OF RED AND BLUE\n",
    "nPlot = 5\n",
    "for t in range(nTracts):\n",
    "    if(t % nPlot == 0 and tractPop[t] > minTractPop):\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,color='green')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "a5d3b510-6d1e-49ae-9a5d-03352e2b449e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "map of tracts that were used less than 0.7 of expectation in PA\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now, where are those UNDERPERFORMING tracts (<0.x usage)\n",
    "maxPlot = 0.70\n",
    "print(\"map of tracts that were used less than {0} of expectation in {1}\".format(maxPlot, STATE) )\n",
    "for t in range(nTracts):\n",
    "    if(tractUse[t] < maxPlot and tractUse[t] > 0.05):  #ignore skipped tracts\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "\n",
    "x,y = wholeMAP.exterior.xy   #turn these on if I pull map back in\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "876011e8-d37e-44a6-8dd9-a1e4d2f51e58",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a map of tracts that were used more than 1.2 of expectation\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ... And where are the OVERUSED tracts?\n",
    "minPlot = 1.2\n",
    "print(\"here is a map of tracts that were used more than {0} of expectation\".format(minPlot) )\n",
    "for t in range(nTracts):\n",
    "    if(tractUse[t] >minPlot and tractUse[t] > 0.05):  #ignore skipped tracts\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "19bfa7c8-0449-4182-8ed9-f5a1c1276f03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a map of 0 tracts that were used more than 1.6 of expectation\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ... And where are the REALLY OVERUSED tracts?\n",
    "minPlot = 1.6\n",
    "counter = 0\n",
    "for t in range(nTracts):\n",
    "    if(tractUse[t] >minPlot and tractUse[t] > 0.05):  #ignore skipped tracts\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "        counter +=1\n",
    "\n",
    "print(\"here is a map of {0} tracts that were used more than {1} of expectation\".format(counter, minPlot) )\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "f3e9bde3-06f6-402d-bce1-62b5631e8db0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CORRELATION OF TRACT UNDER-USAGE IN OTHER TRACT'S HOME DISTRICTS vs. being near a boundary\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(tractUse,nearEdge,marker='.' )\n",
    "ax.set(xlabel=\"tract use vs. expectation\", ylabel=\"number of unfilled wedges - max = \"+str(nWedges))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "6bb5c12a-a6a4-4cb1-9026-d227bc11c2ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.11005146120844314 = PA std dev of TRACT usage.  Here is its weighted histogram\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR tract usage in a histogram\n",
    "n_bins=50\n",
    "avgTractUse = 0.\n",
    "statePop = np.sum(tractPop)\n",
    "for t in range(nTracts):\n",
    "    avgTractUse += tractUse[t] * tractPop[t]/statePop\n",
    "sumVarTractUse = 0.\n",
    "for t in range(nTracts):\n",
    "    sumVarTractUse += (tractUse[t]-avgTractUse)**2 *tractPop[t]/statePop\n",
    "sdTractUse = sumVarTractUse ** 0.5\n",
    "\n",
    "print(sdTractUse,\"=\",STATE,\"std dev of TRACT usage.  Here is its weighted histogram\")\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractUse, bins=n_bins, weights=tractPop)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "121f9869-3d25-4594-93b3-64a57a95aca8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1145569784505047 = std dev of PRECINCT usage.  Here is its weighted histogram\n",
      "this is a histogram of PRECINCT usage by precinct for PA\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR precinct usage in a histogram  \n",
    "n_bins=50\n",
    "avgPrecinctUse = 0.\n",
    "stateVTDPop = np.sum(vtdPop)\n",
    "for p in range(nPrecincts):\n",
    "    avgPrecinctUse += precinctUse[p] * vtdPop[p]/stateVTDPop\n",
    "sumVarPrecinctUse = 0.\n",
    "for p in range(nPrecincts):\n",
    "    sumVarPrecinctUse += (precinctUse[p]-avgPrecinctUse)**2 *vtdPop[p]/stateVTDPop\n",
    "sdPrecinctUse = sumVarPrecinctUse ** 0.5\n",
    "\n",
    "print(sdPrecinctUse,\"= std dev of PRECINCT usage.  Here is its weighted histogram\")\n",
    "print(\"this is a histogram of PRECINCT usage by precinct for\",STATE)        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(precinctUse, bins=n_bins, weights=vtdPop)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "f6f03dcb-c722-4789-b47f-fb7aa3be97bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here's a look at numerical stability - number of loops, avg =  6.785\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "avgTLC = 0.\n",
    "usedTracts = 0.\n",
    "for t in range (nTracts):\n",
    "    if(tractLoopCounter[t] > 0):\n",
    "        avgTLC += tractLoopCounter[t]\n",
    "        usedTracts +=1.\n",
    "avgTLC = round(avgTLC / usedTracts, 4)\n",
    "print(\"here's a look at numerical stability - number of loops, avg = \",avgTLC)\n",
    "n_bins=20     \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractLoopCounter, bins=n_bins)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "974631b2-10ef-4847-af2e-a1e26b6fa768",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district pct GOP by tract\n",
      "statewide vote is off by 0.00457 0.49858 HD avg vs true 0.494\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR HOME DISTRICT red-blue lean in a histogram  and check the statewide vote vs. HD average\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district pct GOP by tract\") \n",
    "print(\"statewide vote is off by\",round((stateGOP2-stateGOP),5),round(stateGOP2,5),\"HD avg vs true\",round(stateGOP,5))\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDvGOP, bins=n_bins,weights=HDweight)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "720d9eb5-11e8-45f7-b402-a0048e9ddb99",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculated statewide Hispanic pct was 0.06302, should have been 0.06802\n",
      "this is a histogram of home-district VAP pct Hispanic by tract\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR home district pct Hispanic in a histogram\n",
    "print(\"calculated statewide Hispanic pct was {0}, should have been {1}\"\n",
    "      .format(round(stateHisp2,5), round(np.sum(tractHisp)/np.sum(tractVAP),5) ) )\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district VAP pct Hispanic by tract\")        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDvHisp, bins=[0.1,0.15,0.2,0.25,0.3,0.35,0.4,0.45,0.5,0.55,0.6,0.65,0.7,0.75,0.8,0.85,0.9,0.95,1.0],\n",
    "        weights=HDweight)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "1ab7704f-a226-45a9-98a6-d0684522c1eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a bar plot of seats by VAP pct Hispanic for PA\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ALTERNATE VIEW OF HISPANIC VOTE: HOW MANY DISTRICTS PER 10pct BIN of PCT HISPANIC VOTE?\n",
    "n_bins = 20\n",
    "HispSeats = [0.]*n_bins\n",
    "binMid = [0.]*n_bins\n",
    "for b in range(n_bins):\n",
    "    binMid[b]= float(b)/n_bins + 0.5/n_bins  #centering each bin\n",
    "for t in range(nTracts) :\n",
    "    b = int(HDvHisp[t]*n_bins)\n",
    "    HispSeats[b] += HDweight[t]*nDistricts  #multiply by nDistricts to get number of expected seats\n",
    "\n",
    "print(\"this is a bar plot of seats by VAP pct Hispanic for\",STATE)        \n",
    "fig, ax = plt.subplots()\n",
    "plt.bar(binMid,HispSeats,width=0.09 )\n",
    "#plt.scatter(binMid,HispSeats )\n",
    "ax.set(xlabel=\"pct Hispanic\", ylabel=\"number of seats\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "3ef49161-368a-40a2-83ff-92187edb3462",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using a threshold of 0.35 the number of Hisp districts should be 0.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Where in the state do we see Hispanic-heavy home districts?\n",
    "minHisp1 = 0.35\n",
    "minHisp2 = 0.40\n",
    "sumHispDistricts = 0.\n",
    "for t in range(nTracts):\n",
    "    if (HDvHisp[t] > minHisp1 and tractPop[t] > minTractPop):\n",
    "        sumHispDistricts += tractPop[t]/avgDistrictPop\n",
    "        if (HDvHisp[t] < minHisp2):\n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='orange' )\n",
    "        else :            \n",
    "            plt.text(tractCPx[t],tractCPy[t],'o',color='blue', fontsize= 12)\n",
    "print(\"using a threshold of\",minHisp1,\"the number of Hisp districts should be\",round(sumHispDistricts,3))\n",
    "x,y = origMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "a92133ad-dc95-4aa1-af02-c1d71296cee6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a bar plot of seats by VAP pct Black for PA\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ALTERNATE VIEW OF BLACK VOTE: HOW MANY DISTRICTS PER 10pct BIN of PCT BLACK VOTE?\n",
    "n_bins = 10\n",
    "BlackSeats = [0.]*n_bins\n",
    "binMid = [0.]*n_bins\n",
    "for b in range(n_bins):\n",
    "    binMid[b]= float(b)/n_bins + 0.5/n_bins  #centering each bin\n",
    "for t in range(nTracts) :\n",
    "    b = int(HDvBlack[t]*n_bins)\n",
    "    BlackSeats[b] += HDweight[t]*nDistricts  #multiply by nDistricts to get number of expected seats\n",
    "\n",
    "print(\"this is a bar plot of seats by VAP pct Black for\",STATE)        \n",
    "fig, ax = plt.subplots()\n",
    "plt.bar(binMid,BlackSeats,width=0.09 )\n",
    "ax.set(xlabel=\"pct Black\", ylabel=\"number of seats\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "b1e9c437-ddf7-4202-aff0-19f338a5d013",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Black candidates would expect to win 2.771 PA seats out of 17\n",
      "Using simple Alabama-style of Blacks voting 0.9 Dem and whites voting 0.8 GOP\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Where in the state do we see opportunity home districts?  #NEW FORMULATION based on ALABAMA\n",
    "isOppor = [0]*nTracts #will track whether this is an opportunity district\n",
    "blackFracSeats = 0.\n",
    "blackPctDem = 0.9  #fraction of Black voters who are Democrats (est'd from Chen / Stephanopoulos)\n",
    "whitePctGOP = 0.8   #fraction of White voters who are Republican  (\"  \")\n",
    "#minBlack1 = 0.30\n",
    "#minBlack2 = 0.50\n",
    "for t in range(nTracts):\n",
    "    pctWhiteDem = (1-whitePctGOP)*(1-HDvBlack[t])  #this is the fraction of voters who are white Democrats\n",
    "    pctBlackDem = blackPctDem * HDvBlack[t]  #fraction of voters who are black Democrats\n",
    "    if tractPop[t] > minTractPop and HDvGOP[t] < 0.5 :  #Dems would win this Home District\n",
    "        if pctBlackDem > pctWhiteDem : #Blacks would win the primary\n",
    "            isOppor[t] = 1\n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='blue' )\n",
    "            blackFracSeats += HDweight[t]\n",
    "        else :  #the White Dem candidate would win the election          \n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='gray' )\n",
    "\n",
    "print(\"Black candidates would expect to win\",round(blackFracSeats*nDistricts,3),STATE,\"seats out of\",nDistricts)\n",
    "print(\"Using simple Alabama-style of Blacks voting\",blackPctDem,\"Dem and whites voting\",whitePctGOP,\"GOP\")\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "c5125128-e4bb-4337-bc5e-3f48d7cf404f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here's the PA map with Hisp+Black greater than  0.4 or even 0.5\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Where in the state do we see Black+Hispanic heavy home districts?\n",
    "minMinor = 0.40 \n",
    "minMinor2 = 0.50\n",
    "print(\"Here's the\",STATE,\"map with Hisp+Black greater than \",minMinor,\"or even\",minMinor2)\n",
    "for t in range(nTracts):\n",
    "    if ((HDvBlack[t] + HDvHisp[t]) > minMinor and tractPop[t] > minTractPop):\n",
    "        if (HDvBlack[t] + HDvHisp[t]) > minMinor2 :\n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='blue' )\n",
    "        else :            \n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='gray' )\n",
    "\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "58119ef8-13ef-4135-9843-6518025dda07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district population by tract; avg =  764864.1176470588\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR HOME DISTRICT population in a histogram\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district population by tract; avg = \",np.sum(tractPop)/nDistricts)        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "a = avgDistrictPop\n",
    "ax.hist(HDvPop, bins=[0.9*a,0.92*a,0.95*a,0.99*a,1.0*a,1.01*a,1.05*a,1.1*a])   #n_bins\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "1f9101bd-108a-48cb-bee5-3f4e04c18132",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district area by tract\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# more HOME DISTRICT STATS in a histogram\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district area by tract\")        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDarea, bins=n_bins)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "d8167a80-2b91-4adc-ab34-9e010838213d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What is the correlation of HD area to red-blue lean?\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(HDarea,HDvGOP,marker='.' )\n",
    "ax.set(xlabel=\"tract area\", ylabel=\"home district pct GOP\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "cf95cc7c-e58a-4174-94f6-b6d4c7ccb0ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What is the correlation of tract usage to red-blue lean?\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(HDvGOP,tractUse, marker='.' )\n",
    "ax.set(xlabel=\"home district pct GOP\", ylabel=\"tractUse\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "617384b8-1e0b-45a7-ae09-babe8dd9d429",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Aenrfe2WlK69DlQlRGd6FUQPf+Mwcy/8AdpnOSEugiycYv9ScseseCM6e904k9y2fg7XvNcFkYOuBE6hr7vQ1x9U1d7qcxfctn4Mn3t/vCAUv44si+NyCqZg3tcQVBHDLpdPx0s52ANbEvrO1C3XNnSgZW+BsU99Rf7DbV4gQgLJxBThxOpbyXvXMUtxYPQ11LZ0p2pESACtvqcY7jR04cvIMlleV4+TZOAhWFJmubXqDGxQGAfctn5OS8OgnUA2DXMUZDYOc/iEJk7FuSwvWb28FiJzjJBJuAaiujV8jNBWxtqu1K6Vwo99iIGiiHEpfwUhpejbQcxJ/SXYMu7M9n/BT73WHq1p9D+YG3LCjzREigDWhqlV6xCBce/FkvLXniGNG8kM30ejjmT+txDXxqRDRJ97f70yWZ2OpEV0bdrQ52oWyyZ+Jma6wXT96zibw0s52XDGnzJUN7uWNj45gY2MHvrRoujMOk+HUtvKDAV8hAgBb95/A+ro23++698pKJ5dEjWfPoZPOJG5QMkyZAHxuwVR8dOhkSrRZgoHH321yjS+RMFEzsxS72rpd+95/9VwncMBralPnEktYng6ldXrPu6y40LVaNohwy6XT8equ9pTQ8J9ua8Fz3/yM72LAWxJngx0YUlZc6IR/e82XAxUK2a7qwxQR9Tt+2CKl6RB/SXaIIMmSzU3HnUk2biYdsUE3uB+qP7iX5OTKmFJS5ApF9UZHAakJeXHTqiEVIbe55kzMxJPvN7k0Cgawy9YQ3mg47JosI1rk1kDYdqATO5o7U5zcOv0Jxsu2lqKPZTDs6zjlu33BtBL87W2LALgn1P6Ecldb36rMXgzgncYOzC4f53s87/hMwAlB1rWohkMnUT19ghPVZkSSGokiYgCGkQzYSCRMR0AQrOAGfbVsMjtanZe4icBS97o/BwB+ur0Vph1QoQJH7l5a6ZhHByoUvGHQA13Ve4WliozT8dMaAH9Na6ABAqM1oOBcIYIkS8qKC0GaD9dkTnuD+6GKAiou8JhuDCLcvrgCty+uwOMbP8WRk2cwd9I4vLKzHepTc8qLUTq2ABOKojC0Cc00GSZSJ799R0+njOONj47gbTvDW2cgfgovCVZJiRwoTAZ/9HDsO3oKz2xpQUN7N472nHUqEOvfS7Amk20HOh1NIUgweWEG9hzugQG3Sey9T445IcoGAYtnTUR7Vx/aus4433nXFZUuX0fj4R5XHohaGRt2WG8mjvWc9d2+ZHayykLcZJejSAWOzNAc8gMx9XjDoH2LdWr7+k3WXmGpL8gUXq2hrLgQj76511Wx2Vt8NKhKst9Y8jGgYKQggiQLVPgraytI9fAHrZ68D9EzW1rw3LZWVzZ2d5/bdHPLpZaTtqcv5vhDvGaUA8d7fberSCW/DHkvSVPL0BE1gNUravDctpaUsWVi8vhCHD3Vn3nHDMQSjL94cbcrpNpLgi2fTUHU8DUxhcEEQD4fZPv4etSaCkSonlHqmryXVZVj9YoavF5/CDfXTHfeW72iBivtCsUqkMEwCBdPGY+Pj/Q49+A7jR0uX5eOqrLgBxnuDouqhprSgNOZevQwaL1Yp3cMz2xpcQlJfXG1rKrcJSxVMIY3iMCvgKb63oE0DBPn+tCSd4Kktz8xYkL0vHkEs8uL8eA1ydyOoI54KiO5ZkapMzkoqmeU4kPPhPvah4dgcnvgij4dphkcgaSjJlcKcDLrk+9AhnHDJVMxf1oJTp+Np7wX5NBWDEaI6Dk4Ohzwt44K5/6NT45NNt/tuy/BCesGkn4bIgLZ2tu2AyecVfm9V1Y6uSs9fTHHbFYytgCz7KKTSrMIUxnYW/GY/X4IVlqt5XsD/DtzejUFPyFS19zpKgza7ynr4xeMofxDflqDbqI0KCm8wmpS4lwfWvJOkDQdO4V/+kWj40j22lFzhZ9Kbpm1rBU/w6oxtfq1BufhD+qIpzKSIwalRD4tryrHnkMnHce7srsPVk8I+znHU5DG/KRyGjbuPerr0AaAmZ5yKL/86Aje+rjDnRtBljP7zT1H/A6RFpUK4jfMCFkT3daAfJZMRAxCzYxSPLu1ZUCfKxkTxdSSIlwwrjDUd+sCtD/mzlbXfwC/fjJA0mSqzGYFEUJB1O0sz9QDpacv5gqaMBkp1ZXj9n0XNy1tYkNAeZcw/oUXdrS57nWDyBmnMotp8Q4wCGho7w4sTJpOeIVxmotzfWjJO0GiJhGvYzuXBDWoWvVqg3tVB+vhf3zjp+iLJZwVI5C8cfWM5LjJKavYhkMnserLNahv7wbB0lBUiRMy3M7ayytKsXOA5qJsSJiMjpNnXLW1vFRNGpca6eQRltNLx6Dp6KkUbUTPa/EjYhAeuHouft5w2DHlKQiW+Wj7gc5QmoHfPtfNn4LO3v5AYRpEz5k4Tp2Jo+VEb8bvnlNejGZt7CasJMxNnx53mZ0I/n4Gv8TY/gRj6ZyJuHb+lIyhvvqKXh9rxMe05b1f05lo01HX3Innt7c6r/Xoxsfe3pf8DnswBixhwICv1pAu3D6s01yc60NL3gkS3cSiHNu5vgmC/B0xz6qcYNmalR9DZa+PsbvlrbylGn/50m7XROWddN775Bg2fXrcpW3pJU70goU/fi99GG4u+LCtG5EIgX2S9iIGcDaWyHgM5Wz2kk6IAJZAevLXB/ClmmkpgkSNJf0RklwxJ1Vz2dHqr01EDYJpsmP2CzKdhfEveccNACfPxnH/1XOdcO+CqGX69Ctp4k2MVWw90Imv/FZFyuTcHzOxZuOnOBNL4Oaa6c49tayqHEUFhitKys8fsWbjp/jVxx1gZlcDNN3PAaQPLFHajXPsyono7O1HXXMnllWVp2jmqrEYgJQkX79F3RsNh/Hff7oTN1VPwyNfWhDaaS7O9aEj7wTJjIljreQvT1RLtqQL1Q1SgwuiyYZOUcOKwKk/2J3iVFZVc4H0PgGFV9vSb3hlL/+uVnAxDGT/byCr7aiBlBBjBpBIMG5cOBVVk8bh1Q/b0dkbQ3FRBCdO9Q/arBSW/riJdxo7sj5OXUsXSooi6DmbFHzHevqdRYCCAMyZNA4FhpWJfvJMqq9HMVjz4/PbWy3twLb3X3fxZJcQ8RYLval6Gna2dqGztx/dfcnxPPnBftx7ZSXKigtdgvUNbWHzdmMHHrr2QgDA7YsrQPa/QRPqe58chWkyIgalJKKqemIzJo5N629YVlWOqEGOuXbrASthVLUjXr2ixtXqeM+hkwD8kypXv9rgLDhicRP/8Poe555TpjpvJYDRzkgohZ93gmRMQQRfuWJWxgdgIGSK4AhSg9c9kOwzcYdWHl51JFQYmtkgk4NZoeo5+eWjBOWdTBgTDZzo2PlfeALcIGAAb33cgaqr5+Jw9xkk2AqC8DK2wEBhgYGevvigAgUUXnNRV1/qORKCAwX8SJjsEiJBMILzUwCgYuIYHOw6E/rSFti5JPr+uiaTYMuv9O4nR/H0/cuccOAwfrKmo6dQ19yJzt7+wPvsjY+O4Fd7joBsLUvvSOlFDyZhtgqKek25z29vxaov16T1NyyZXYY7a2e5Ek0ZliDasKMNf3fbItS3d2Od/X48wa4Kyeqe11sLAEAkYqDlhFvD+3nD4RRBoj8/6rxGizlrpESf5Z0gaTp2Cs9ubUn7AAADk9Kqb7huB/ZzKHpXiMuqyp1EN8W9V1ai5fhpPK5lpetmg88tmOpq6+oHAYhGCO1dfVbfEs3hqDrxec1qANKuloeahMkZM9z7Yib60pirigqMlH7tisIo4fKKiZhYXIiu3syaDsMqtd9x8gz2Hzt9zq5F+fgiHDvd74TJJkwTaVxIME125fkEoUrFPKfVJ8uEycB31u/CN66uchWT9JJgOHkkfve7rgHpraKrp0/AtgMnXCbIhMmuhl89fTE8+uZelxkNsBZ9qsyO7t/56fZW3LHYMuO9sKMtsEKyEmo61148GRdOGue6D2+qnubax9UkzC5Aqld3CFvSaKQyUqLP8k6QMKd2BfQyECmtHIGO0zFDzHyYY99YPQ1PfrAfsYSVLT1/Wonz3kPXXpgsdeIDAbhwyngcOH4a67a2OLH1Jls+hO+9XA/mwUdxjSSChAgAxOOMxZVlaWt66RCAjXuPIp6wKxafI5ZXlWPlrdUpBS7f2Xs0JegAsOfvEPZFk4EXf9MWWogo9h09jZWv1GP1lzPn7vg59OuaO3HX2k2IJxjRCOH+q+bapXQYT206gJW3VKO+vRvr69pcUWJLZpeh8XAP/vEXjQCS/kElTHStfmNjh7MwiCfY0Uq8FZL1UvzePjgAMKWkyNE+ft5w2PGR6LgmWq0cTX/cdDS9cx0BOpSMlOizvBMkRFZUBxH53lzAwKS07ggkAF9dkt5c5j22qlnkbdGqMqe9FWCXVZXjb76yCH9p9xXx4jWlmMzQ58WENtbRIEyCMJFa0yodU0uKcFhldZuMBdNKBpULMlCe/LVVrVi1wN3cdBy3L64AwwqZ9SPsb9fbnz50wFpgp2o38QTjyQ/2o0m7jyIG4dZLp9s5SezKZdrcdByNh3vQ2duPjY0dTmRgPMF4dfchx6wWi5toaO/GzIljserW1NW8t8/O6/WHnMlZ1+Lbu/pSkjOBZNXiTXaFZGU6u31xhasPjjofZZF45EsLAv0iQS0RiJIO/nMZATrUjJTos7wTJDNKk852PWdDJ6yUrmvuRHtXn6uD3B1pzGXeY0cMchVCVNqJ19nZ0xfDPT/e7Ixn1a3VqJo8PnQJDq/AUf6AgYap5hsDOb3DWmkQk62yKEMtbC8oLsDUCWNcAkqZoB7f+Cne+rjDcUzfcun0lM8rDeDCSeOGRMipvBM/f4j33kqYjJ83HMbqFTUp/dz10ibee0rXqgyDApu3Aal9dm6usa6BqmwdSzAKIpTiU/GaqPUxqIWYanvQH7OqKIctjurNhtfD6vUEyXMVAZoLRkL0Wd4JkrjJiNqmnnT+jExS2mU7HUAHOXXsDTva0KD1oTgbs/JHLps1Ebtau1yfefPjDse+2x83XeU6BkrEAEwzvFP5fGWoS70AwIneGPp9HCDeasVx07+w4oyysRgbNXDslH89rEHDCKWBnY1ZGoW3gKWz6GH/8jGAXbnhgmJ8evS0y1GuPy9K+1DlXeZPK8Fjb+/DztYuJ2KrP8GWY/0B/+dzc9NxsCZJVKBKNitvbyKn8q+qsjNDHQF6PpJ3gmR8URRmCG0jk5TWTVRhOsh58ZYrZ1jRNm98dAReE/3BTv+ch8Ewt3ycb8FF4dxwyifSK+zv6eczCUMmzcoE8HGAELGtOUiYqV0bvUmHBCASISR8coQMg7D/+GlX1NX6uraUxZfqs6Mv1IjcDwQhOHhFz5MhsiopNB7uCVWEMR1+JumZE8e6NLThXtXnM3knSIoLI3h8CGyCZcWFTnnvgTqpXtjh3/MCSPYF1zvxpYtcGihnQiT8jQZGuw9oINy4cCqOnDwT6DgPulYGAX/zFXdorV6La8nsZEXghGmbyHzCjA0CLp4yPkXjiSdSs83VM6lP3AYsc59pcoopS4X1qoXhugeW4en7lzmthN/46Ah+aS/OsglvzWSSBqTNbjbknSABsrcJqqq9JluhmCtvqR7Q8TJNcAzggc9W+ZbyCMJAuKzsoKzw0cZoFyIGJcu66EQjVqdF/V7Y3HQcl0wrQUGEfE12Qf4yZqvirwqtVZNou93id8nsMqcisFoA6YdRznzTZF+Nx1svS4XY3lk7C9UzSl39c667eHJKF0fAvShT/qa/vW2Rq9YXkDlSMxO6aay9qw/rtra4tBM1jlw05DofMIZ7AMOBvlpi5pSIkEzcsbgChZHgEFOC1SL1wWsuzHisggjhoWuq8De3LQIFH1IYZSyaWeqatC+aMh73XlmJ5x5cjr+5bZHLPHryTBxbD3QG+n3S9Xnp6Ys5danuWloJBvD0lhbcuebXeOAn251ckQhZ92Jh1IBBVlmYzy+Y6oSa+2kp118yBYD7eepPsJP7tPKWaty9tBJgxpt7jjgVhL1j9HutytjrZArNz8SS2WV4+PqLcPviiuQ5Rw0QUmt6KeqaO/HY2/tQ1+yfx6SE6P/8ZSO+9sTmwP1GO3mpkWRLuqiuTKuLZ7a04PX6Q/jGVXNx8mzcUpHj7sSvgkjSQdhy/DR+WteGE6f9hVUswfjx+034/opFMACcH4ar8xNlgjIImDtpHOrbTzqqxKcdp1AzY4KTzBcmcTUMP37PCqEujBq4Zt5kRxiZnGx1vOrL7kguPSfm3U+OptRAmzAmijOxBN7acwTvfXIU9y2f4xJmKlS4s7cfMyaORdz0D46pa7YKbEYNy4djEFAzoxSANel/dUmFY5ILE5ofFr/SKxs8Nb3U+DLljI2UhMDh5rwUJEERIJlunGe2tOC7L1rlT9775Bj+7rZFuMPubldWXIiG9m5XuZS65k48telA2sQ7wHqIXq8/NKDaWcK5Y+H0EhREDOzrOIXTPqVgwqKbabxRXYzktvc+OYalc4ZmMlL3VCxu4sjJVLNof8LSyJUTW19Rq+fkO+t3uQI8ppQUOdFb/TETDYdOuvw03kRH3TehzGp6J0jDIBiGZR3QQ/p1k1yY0PwgMi0Og+aDMEIim4TA0WQSy5kgIaInAdwCoIOZa3zeJwA/BPAlAL0A7mPmHbkajxc/P0umGyco4SroJtBLr6QjYgDl41KTKyOGZYdWCWIiZ4aHjw7lPrHRy9mgQmeDxDAoMEDkkyM9eOztfa6ug7oTumxcIaAJkvFFUVeelCqdooTFnbWzXHXwVLj8+ro2rNvague3tyLByeRaU1tB6c/dUCTbBbWACOrzrhNGSAx2jCOlRtZQkUuN5CkA/wrgJwHv3wxgnv3flQB+ZP87bGQyeY0tiLj2VwlX6n1vYUW99IoXgvWAXjR5HL5z8wKsfrXBd5/fXmS12Y1EjEGHj44UwhasFCzTV2tnL06cjmXe2cPsC4rR0tmbktgXlGeiNKEIJR3uuhPaa9ra1dbtMtOVjC1whAUhWUxVfyZmThyLeCJZqiToNvD6QbINrAlqARHGHBVWSAxmjKPNJJYzQcLM7xLRnDS7rADwE7ayjzYT0UQims7Mh9J8xiEXamEYk1c0YhWvu+uKSlf5B311sfKWajy3rcVJwvKjIGrgx79X63xHUTQ17iFupppARhoDEQ4iRIIZXxjBKc1sFvZ397v+3WdiLiESNpQ6YYetA24ntBeG5YxnZkQiBna2dmFXaxfeaexA3LRqZ628pTql2KiTHwJLQ0ok2GltoPwgC+y6dGEq9oaZA4IWh2HNUbnKGh9tJrHh9JHMBNCqvW6zt6UIEiJ6EMCDAFBZWZlTtdB749Q1d+LRN/cmk7ZMxheqp7kKvLmiVuzCipmquyY8Mfg7Pdnw+cKU8UWu8iRCOPTaVwmTXUIkEwumlSCWMHEmlkDJmIIUTaOr163FuOqQZWDJ7DKn0+IbDYcDhf+8KeMRMxn7j55KCQroj7vbByvHu56zEgFwz5WVTgdQte+Hbd24Z+0mp0pvNGIAzK6yLACcPJOgci36+fgtDoe7PtVoM4kNpyDxC3b1vW2ZeS2AtQBQW1vL50ot1H80hhUr7bd60FcXejE4PwjWKlI/jrdwJJA//pDRIEQmjS/EwukT8K5WJyosA02cvGjyONy2uALLqsqxuek4Emb7gH/rWRcU4529R21tIXNe0UB+o52tXfjOzVYBxCfe3x+4X7pyLFal6gSiEQNxu0CiasqlclYSJmPGxLG498pKzJ9Wgkff3Iv3PzmmdZpMFopU16c/ljS36b7HTHOAn1YxEupTjSaT2HAKkjYAs7TXFQBC6fPnqnSyKzuXgKsumuQ029HxFoZb9WpD2sz3JbPLUFpc6MTV62UhDCJcPHX8sDh4z1eOneoflBABBi7wqyaPd+7XjY0dA/58YdTA5JIi3340Q0HMLus+c+JYVw/5gbLtQCeiRrI68apXG3B5RanvQkpV/d124ITL9KV6tzghywCO9Zx11QfzRoiNdkZK2XgvxDksIWv7SF4LiNr6bQDfghW1dSWAf2HmpZmOufDS3+KHf/i8s8LJpWqqNBL1o4VVI+uaO10tQNNRaJeFGEgnvKFGypEMjMFeL5Vk6Ff6PRMRAu5eWumUF/F2CxxKVB+SH7+/3zVOA1Y/9Yb27sDumWGIEPD9ryxK6f/xzJYWfO8lq4W0al3NAJ61s9AN+xo4WfqR4N72o5nB+kiIqI6Za3MxplyG/64DcB2ASUTUBuCvABQAADOvAfAzWEJkH6zw36+HOW7T0VP4p180wrBvxlzeQGGrCHvfXzK7LHT4Zixu4tE396LygmJH7T/XiBAZGIO5XtMnFOHwybN2y+P0R/BzoN962QynyyABWHVrtZO3NKEomrFb5UCIJxhr32tyjcGAJWBqZpbirisqUd/ejWM9Z/Grj48gYVqh6tBC1dNh2qVbvNS3dzt5L3G7yKRfLonK3RpJzuZzyUgwy3nJZdTWPRneZwAPD/i49n8JBr730u6cN6Pxc77r0STeaK3O3n6UFReGTlxjAO9/cgyRCA1JVNO8yePwiVQH9iViZ1APB4dOhvdT+N0HL+1sxys7250KCtEI4Yb5UzC5pAg3Vk9DZfk4PPbOvsAQ8agB3HLpDN9oMIOA6aVW73m/MRAsTWTP4R6ss9tcK6f3O3uPImGaMAyrz87r9YdcPUnU5wEtYZGAXa1dTr0v7376a1VYUpWm1xdrA2EkRjqNJvI6s91knFNnkzdi4vbFFY4P5WzM3WdkIGWzGAi1kguDCJFghkqIhC2wGYaBmMn074wnGL+0o6Wer2vDugeW4YPv3ICrf/CWb2FPBmHp3HKc7k/gV3YDLoOAzy2Yim9eeyEaD/c4VRu8FEQIUyaMwYdt3Y4DXOVjxBOWvyKRsCKzvM2tlA/DNE3HHGay1XLhVx8fwXPf/Izz/N6+uALP2+0ZIgahekapU2C1P25i24ETg1o4jtRIp9FEXhdtNAhpux+mK7aWjqDPusJ8badg1DZ8K01JoZK1KiaOwYQxuZHXfrknQm6ZU16MB6+pQmGGa18S8JsbBFdBRrUtG/SJfYFdq8qLabeTfWvPEUTI6hvy/a8swmWzJgLwNzWpQpLfuGoufvWxOzCgrLjQcfyq4ofLqsrR2duf9AUBuHreJFx78WRfIR43gdWvNjjP2ZLZZVh1a7WTo7L6tQZssKvy+hVUDEtQUqIwdOS1RlIzszTQbzHYFUi6z5YVFzoqv8lWu07UpnbIU0QMymnZ96EuoyFkpuVEL5789QFcXlGKE6f7A5uMnToT993uNVsxsm+ZbBiEXzYcRk9fDFNKihwfi4p+Yjv6yelRbmeWexMG9R46ADCuMIKaGaUpeVEJtj779P3LfH2IelTRzTXTserVhkCt68O2bnztic3Oc6ZChNWkTwifPBjESI10Gk3ktSBZHnBDDCTWOl1DHu9nO3v7k6Uh7NfKAeqtqUUASoqiONE78BIXwshFaaOZIvIYwKSSQhzrGViLAgCYOXEMSscWhAoBnzy+EEdP9WNXW3dK4yvVH72ztx89fTHHIa+H0eoJg9//yiInagoAdh/sRn27f3KtejbUpKx3MdSFy+am44jb6ggBuMTTEthrKmvv6kPUsKLaVBOs27N0rg9FzS4hPXktSE6e9V/1hV2B+Gkf6T67rKocRQXu99RN+uibe122YQZEiAyS2RcUo/lEuIZgI5mBCBHld4kawMPXz8Pq11Jrr3mJEFBabAkSPxJmsrLvY2/vc2kqAFwtbcuKC13JgR/sO2ZpTx51STXkKogaKCsuxD1rNzmlgPQuhkqIqH4n6pmZdUGxS5Dox0qWITJw19JZrrBevVzRYPu2iwDJHXktSNKZlu9YXOEq6e6Hn/bx8PUXBa5eglY2KqFqS9Nx3/paUSN9trvg5up5k3Bwe2uoAITiwgh6syjtPhKIkNVRs+HQSdxcMx2dvf1pc0QiBIAsP8KBY6cC9yMi9PTFnMq+0YjhJPO909iBb1w1F0/YuSKrXqlHQ3s3bl9cYd3L+0+4xkBwazjLqsot/4X2GwV1HNSjGVe9Uu/sXxCxKgWrcF71LCYSJmZOHJvy3A7GZC3RWueGvBMkhOQq5naf/gTemy1dD4Mg7SPd6kW9pxzy6gZdMrsM6x5cjg072nCs5yzeaexwqpyKEAlPhCzfk0FtCBPPdOYcCxGDgNrZZaGSTcNAZOWIPLXpgBOZ5G0U5aW0uBBdvf2OdnHjwqnoOHkG9Qe7rcQ9AwBb992ad5scTeGyilJss8edMBkNh046uUuqs+GGHW14+v5lrqZSAHBpRSlW3upuSe3teKieSwJSzGYPX38RvvvibidLnQAsnD7BtdDLZEVIZ3b2ExgSrXXuyDtBUjV5PB7+4vzAFcZA/CNDXThNb4hz3fwpWPvup6F7tg8Xk8YXwmQeVLnynEBWYppuVweCRcq5FtGqUu1QlMSP2GVAVOFG5S/YlCGqSHXbVKVGHrr2QiyZXeZ07xxTEMFbe5KFFFUx0R0tXa7vvrlmOrYdOOH493R/xR2LK7B+e6ujcfjV1lLhulaWOeEuuw8JkNpxsK65E+vr2pzfi2H5YHRHe6ZnMWjhF/Q8jtS6VKORvBMkxYURp5ubHwON0BjKwmnPbGlxypwUFRi4b/mc0BnHC6eXZF1fa6ClOwwAj/+uVTFBt3UPJ6YJJ6w6lrByHRbNLMVOjyMZSBbAPJfDZlh1pIYi8Npp7MSMiB3yWhA1MHXCGACp5+tFr/2m51tEI4ZlTk0wTKiQY3JqZxGAO2tnOT4R1XQqkXD7/u6sneVEJOrVqhVLZpdh3QP+k79XKDz29j5ncaDG4H1+Mj2LQcIm6HmUaK1zR94JkkyciwgNvxu0rrkTK1+ud8xY/TETJWML8He3LcLr9YdwJpZwzAoKg6y2pV+5fCZ6zsZdgsQga/XrnSinTShCUTTickYTgL+9bRHeaexwktQyQQAevKbKmYRAAxVDuSEaIZQURRG3V+gJhq8QAaxrM23i2Jw1/LqguABdfTFHC9EJ8mAsnVOG7c2dobSViP0jF2h+BDXZvbP3qFOY0e9QhRHCtz9/MQDgsbf3ob2rz+VjuHtpJWZMHOvUpFPdD9U9Wz2j1DHN/t1ti1Azo9TJHlfHrJ5RmhJc4iVo8vdu15+ZiF0aXkVmDWSC9/u+dCZqidY6N+S0aGMuqK2t5cc3vDHsN4fXJvvY2/vwP3/Z6EwgUYPw3DeXu+y1fqt+AlBUYOCz8ya7+jrcuHAqLp810RW2CQB/5yMwvrBwKtb+Xi3qmjvxX9b82hE8BKBsXEGg2SpqEFavsJyn//iLxtDnbpAVxplOgxqsWIrY9v2h0jLChOAumFaCIz1n0Hk6FpgPdOul0/HyrvaMOR9Rg7C4cmKgD0Vdl0KP41oJdG8zJ1VN2mpRYPlnLppa4vj+nEgngwAiR6vw8wfUNXfihR1t6Og5i417jyKeSDrDdW1G7/9x3/I5ThCAt8jiQAlqVqX/PdjnWZzqmcnLoo25orc/MSIcaEErLlUKfvWKmlQzgO2MX2/blZVduj9uouPkGUQNK9s3QsD186fg3isrXWGbhu0/eOvjDtdYJpUUOd/x/a8swsqX6+0S3Ejr+4jb2c73Xz13QOfODBREDBREkiW+vRgGYW55MWIJHlAo72DLmMwpL0blBcUp5eBP+ITGLpxego8P9zhCX7f/L5hWgsKo4crJSJiM3v4Erphdhm0HOp1ufn5nbpqMi6aWYGdbt2/k1Y0Lp+KyWRN9u/1572vdhKtMph8e7MZ3bl7gLF4cLcRk3LV0FmZOHJvicNYnbG/OU8xuQqW2qYoNDKvszxPv74fJHLo8SboJ3fvMKOHpV69Ownvzi7wTJKfOxhEdgQ60MGq0utnvsJMY19e1OX2sP2zrRjRCiJCV1bvyZStM0qu2EwBdiyQANVpZDGX33tx0HM9saXYV4osYqeXLTTt6J50GQQAunDzOyeJmWONNR8LkwKzvdKgSIgNN2m8+3otWH4HlNTERgL0dpwJNT41HevDly2akJPfpGiAR8NmLJqUILQJQWJCsUKs3awKs6/9N2zEOWJOuquarJnGV8Pjom3sd/4feEEq/58uKC2HYYcBEhJoZpS6tIag2HGvjLYgaKB9XmFLeR/2rBwGopMGge3wwUVK6f+NszMT3XrLq1UmUVX6Rd4JkfFEU5ghxoHlXX2FXRfo+DQe7nWJ4qnQFkNQWVq+owTXzJuPIyTNYXlWOk2fjVrSPrQkwgFWvNrhWi2ocL/7moOt7EyY7Wo8iapex0KN3AMsRH4lYUUUFUQMXjCsENMHgNw+XFkfR3eufJBqWL182A/uPnU6ZyMNEb3mVo5llqf4TdZ2DMBl4c096PxMz8P6+Y1igZWkbAK6a5258pjdrUlpq4+EePPrmXlRPn4AnP9jvmDoLIuTkG5lsVYTeduAEnr5/mSMwYDvl27v68MyWFqx+rcHxySVMqzaVfh94ndCEZIhtxLByOG63BV7guSIZHaYnDfpN9IOJklpWVe7Kb1E/zUhaJAqZyTtBUlwYweMjwIE2VPW8oraJKGEyIhEDiYTpPEwJk/GXL+12Vs+72rqdPBodv4eurrkTcyeNw76OZMIawcrRUIKLAHx1SYWjxbywow3PbmtBwrScyWQy7l5q9dX+3kuplWG9ZdmvnFOON/ccyehoTicUXtnZ7lox6/vM8BEMCNi3IEJ4+LqLXCU/wnLqbObcFJMtzaYwkiznoUdQ+ZUL0SvsekutxxKMLyyciiMnz2CXVmX3hR1t2LCjDSYziKxzXLe1BYA76dyrrQBWbThSAigSXG7EW7FXD20mWFFzNTNLUd/enVZQDCZKasnsspScFaUpSZRV/pB3ggQYGfbQbGLUvVm8KsJGTTYrX663Jg6krrKVX0XH+9ApQXU2Zrom2KICA3ddUYk9h5PRO8ppu2R2GV7Y0eYSDAkGjvacRWdvv++kXzGxGC2dvWC2zDbXzZ+i9RJP4p3k083r6pPebnwM4GBnn+tYBsEx8TQdPeWY0vTwVgAuYezHZRXWJOnnn7F82JZmRp7cEWbGnVckfzs9BNxkTvF1eFf+3uvyq4+P2NFzFpGI4TJ5EYCET7CGWhSoUieAdQ+serUhGWJsWifn9+yo6/R6/SFUT5+ApmOn8daeI2BYwQN7Dvdg98FuJ6w4KNpqsFFSdyyuwHq7fLy65itvqR72Z1wIT14KkpFANjHq3s/e7qkppHwcu1q7UsJ59cmH4J9xvLnpeEoRyYj9cAKW8GLAtaSta+7E89tbU8b61p4juG7+FEQNSok4053oCZPxTmMHFkwrcTQexWACsAptoffcthaXmct7rN+0dDnVYhW6gJw/rSRtJnrUIEydMCbFlBY1gBsumYpJJUWomVGKdxo7XEl+ABCNuH87bwj42Zjb1+Fd+a+4fAY2Nx3HYbvplTWPWp9V2qLeITBiEBKczD8hWOVkqqdPcJziyrylSpQoEmb63j1KK9U15a8uqQDB0oD8Fj36eb+wo80pSZQuz8sPP63Er6y9MHIRQTJIsolR9/usn7+lrrnTyScw7BDU/cdO46NDJx3fhS5E1DHKigsR8dT3MhloaO/Gc9tak+1ME+xMLpubjvuWcmG2Huo7a2fh6S0tac/rzT1HnJyLbLJSblw4FdfPn+KEpHpRhQNV73P9e9QErEcEnY25E+Fsaw8idvhzQ3tq4IDJSK7KPSZHRSJhovFwj8snoQczMNy+jvnTShzz1fKqcqcsihcVEq7Kh3jNYyoqr7DAMqdtbjruKr2+YUcb1nsWBREjuHePwq/elaqp5bfoAeywdq1//PrtrVj34PIBaxPelrpi1sovRJBkwWBNbF6hka7kisocVgllKmfg7qWVKath/Rj3Xz0XP7YL8gGWz4ABJ7sZsEJ01QOra0lk97BgtjSDoNBRL7ocurTC6u39dmOHKz9G5yuXz8Dx0/1OEtxz26w2rlNKitCg2eO9QunBz1ahZGyB65qYbJnDVNQUkJwYdQ2uqCA1+a++vTul5In+d8xnsgcs09/Kl+sdB7eqDq1fI2WaUr4O9fvUzCwNvJbkMe3o95musZYVFzq13ZSwU5F9KWHZmslM4b0P/bTsTAumzU3HXdcnpi1Ogr7HD0kezG9EkJxjlA09brIrITDI36ImET1nIG4yZniqo3p9NiVjC/DTby53mRwaD/c44aJqNQ7AyXDWH2R1TP2hVu+rbOmNjR0uk5E+Ge85dBLzp5Wgs7cfb350xHfC3H/sNJZXlTu2+T2HTqI/wdh2oNMKhTYInGBEI1Z5j4RpCcTK8nHo7O3H/GklKWPSx+vKptailPyEb9rEfrJMg7pQcxqcmcmJU02G3rBfIkL9wW5XrsYnR3pSNClH+LDlrwjK21Db9ATXiGE1ervrCstE9dy2VpeGmfBM8N5yPunqXaVbMC2rKkeBnT8FWL+Pn78uTFDKSPB9CoNDBMk5xGtD10N8M/lbevpiru6MyqmqCFpN6pPm6tcanLpOq1fUuGzifklwmRLKllWV454fb3a+87qLrex8hmXHVxOS18ym0JsxeaOY4gl26lmZZtIHkkhwoDPby5LZZVh5S7UjqErGFjjXYnPTcVdZEUpnh2PAiBj4L7bPwmte0n+vJbPLkmG/MUu7I7DLb2Ryaq+aC6eMx4Fjpx1T3Qf7kiYxv8l1c9Nxl9aRMK3cnsYjVufC1StqXJ0N9Qner5xPpnpXQVqF0pr1BUu2IcFC/pFTQUJENwH4IYAIgCeY+Qee90sB/AeASnss/8TM/zuXYzoX+Jmu1MRlempsmGw1H1p5SzWe29aCKRPG+B7viff3O68Jqc7IMCaIpKnI+s5sH3Ld9KYmqXc/OZoiEG+4ZAre2nMkYxiuVylQBhMGnKKGul/kbMwyGQWNWUUu9cdNvPfJMSesVJUAiRrkmIQiEQMms5NjYpBVabrp6KmUHhm6eSkog1v9Fge7+vDsVncrZgNICc3+xlVzU5pKpftNllWVoyDiDoDQQ4Afvv4ix+nuneCVT8UZj0G+ixf9OnoXHOo43gWL3zilcOLoJ2eChIgiAB4DcCOANgDbiOgVZv5I2+1hAB8x861ENBlAIxE9zcx5G7LhV/JB921EI4ZTHkUliKl6SpZ5oBsbGztcDkuvEzfi8+BnskMHPdBhO0mGsV17hRmQrAUVhm9eU4VNTcd9Iqj8W8YyLL+K1wGs8EYuKbOSImEyblgwBWdiCdxcMx3zp5Xg8Y2fOk72luOnXb6HsuLClB40dc2d+IsXd7smaz1jvWZGqVM6R/fjPHTthbh+/hSnUKIKwVXaTKbfZMlsq+TOmo2fYv/RU2g+0QvTTCYs1jV3Bk7wmcr5ePEuOLyNqzKZq8T3MfrJpUayFMA+Zm4CACJ6FsAKALogYQAlREQAxgM4ASC71OhhxvvQvV5/yLcekm7TV59ReB2WKgorYSbNUl5nZiY7dNADnekhT1cLCUDaviy6X8cLQfOpEHDF7DLcWD0NN1ZPc46pPseA4y947O19ruPETSsIwG/smaLGiOAUL9x24ARW3lKNvljC8YHEEoypEwqxqGKiK4pMX5V7I5ZWfbkGq16pdzSFwqiBVbcmOwSqf9U19xZCHOjE+94nR51FyucWTMU7e49i3dZkg6ogH8tAvsOvTM9AGkyJ72P0k0tBMhOAHoPYBuBKzz7/CuAVAO0ASgDcxcwpy1ciehDAgwBQWZldBdJc433oVPkRPQHQ+1A1epoG6aGafr4N7+QT1kQV9EAf7Opzut2lM4n1x02Xf+KOxRVJB3LMP9NZ74OhQwTcvdQ6j+e2t2LbASuMdN0Dyywb/6sNjmYSS7BjwtKFqkLlv+gaweam45hQlP72njMpabo6GzNTEhcZwOGTZ3HYjjrzXmMAKRFLz21rcZmb9A6BQDJcVt0P6x4YWD0pfaJ2heuajL5YwqnddjZmBgpYYGCTu5+m6W1cpcYmrXDPT3IpSFLjDVMXiV8EsBPADQAuBPAGEb3HzCddH2JeC2AtYJWRH/qhDh1+q7109nTA8neo1TkBuOuKSpdZS00WzOyb8xDWDu3nu9Ejf57b3up0udMjnwyttpdexG+vFnlkIjUAYMnsMtx/9VxXGXzDvitUEcEXdrQ5fgkVJvu3ty1C9cxSl4mr/mC3U1/KNNnlT4nZbWJ/uq0V9189NzA/w0vVpHFoOdHrhAjr8m5sgYE+Lf+k4+QZ32usRyxFIoSGQ65bN+X30M1t6nyDtEtldlILBz/t0Lto2dJ0HP12zbb1dW2+C5fBoJvyNjcd963SO1CfW7ZCVRg5ZBQkRFQM4L8DqGTmB4hoHoD5zPxaho+2AZilva6ApXnofB3AD9gqZ7uPiPYDuATA1rAnMBLxrvYyrf68gqB6Rim+++JupzaWXtTu+e2tKT6BMKYKv9WiN/In7unbvWR2GRoP9zgTvclWxrdK5tNbt/oFAADAybNuS2XVpHG4sqrcOYcNnr7fajTeVq+7D3ajob3eEWQGktWM1WfiJmPte02+jajUGJXALohYlXgB+DYD04UIACes1nuN9YglAHjWroNF8K864B2X97U+GZvMrjwV70Td2dufEv583fwpycg5n66G2ZBJ4xioYz2TUBXyhzAayf8GUAdguf26DcDzADIJkm0A5hHRXAAHAdwN4F7PPi0APgfgPSKaCmA+gHC9aUcRuiAoKy5MsbFfe/FkJxcjlmBfk4W+KtRfK/xWi5kif5bMLsNz29zZ7NUzSvGF6mlo7+pzigcC/gEAQKpauu/oabR19Tm9vfUS+PrrJbPLsOrLNVj77qdoPt5rm5zcLWlX3lKd0uSLOZm5rtC1oJROhI3u3i5+3LhwqqMVqFW51+kOJEuFqInUK0SApICMJRgFEav0u34srzlQz1MJCvEG4FsEdKijpLwl37334UB9L5mEqpA/hBEkFzLzXUR0DwAwc5/tHE8LM8eJ6FsAfgEr/PdJZm4goofs99cA+D6Ap4hoN6w55zvMfCzwoOcB9e3dLi0hFjcxpaTImfCDTBaDWS2qyJ8NO9qw70gP6lq6nEl6WZXVPthrSrvrikrce2WlE5mkTDD3Xz0Xm5uOo/Fwj8vkcfviCjy3vdVVul0XVPWe46vXyjekfDAGpQqCJbOtXh1vaAmPEcMayxPv73fyPIKaJf3Fi7uda02weq58evS0a0IbY0dYhbnOYSZSdc291Qr0Y61eUeObpxJ0fG9pk88tmIo+OxJtKFf4y6rKnZprQffhQHwvXqGqKhII+UcYQdJPRGNhLxiI6EIAZ8McnJl/BuBnnm1rtL/bAXwh9GhHKd6y8hGtZ4iqbwRY2ch+Jou65k48+ubetPZpr9bj1Vy+9sRmJ3xUled47O19Luezd2WuH89v0leRTYaWNq56W6jJ0bsiUa/18iYGgKsucvf6UOfd3tWHgghZCYxaMMKN1dMymvqe397qCI2CqIFvXF3l0gb1a6HI5AcIM5GqffSoNv1YenMyvwRA7/FdGfwRA+80diBuhu9qGJYls8twZ+2swPtwMMdTQlWc7flNGEHyVwB+DmAWET0N4CoA9+VyUOcbfmXlVZ6J7g9JFymjT+JBJg2vGUSt8FWIsuVwZsfX4dViHrr2Qt/ikmpCdBzvnsimuF2f3U8g3L64As/XuYsCer87YhAqLyh2zjel9ljEwN1XznKtjjNN6HqRSkKyL0tDe7czUUK7FoqhTLBLd6zBRlWpBMhcZZLfbnf3HKoEQwkNHh1kFCTM/AYR7QCwDNYz98fnu/lpqElXVl6RyayRbuWu8GouKpzXcWB7hJBf2KefWaesuNCluRDcx9HP7dufvxgAXH4Bb3a8XvtrzcZP8auPO7Bua4sV5kuEeMJ0/AjerPPBXnNlVvGbKL3Cc6gS7NSxVPJiNuhRVbmsojuU5z9YJGR45BEmausqADuZ+T+J6HcAfJeIfsjMzbkf3vlB2Kgrv/e9E2I6IeLVXAyt3EgmIQQEm3U6e/tdDZau9rScDSOM1CTo1Zbe/rjDyRmx/BnsaAuGXcdqMBNm0DUPKzyHcgJT0UvpkgjDci4m+uHUIrLpTCrkjjCmrR8BuIyILgPwpwCeBPATANfmcmDnG+kezmwdvIC/5nJzzXSsfq0hUAhlyltQE+2yKqt8etBx9HML8gvoY9SrAuiJhwbBaUesorb8nOjZXvOw4x0KclHUcDSbi6QI5MgkjCCJMzMT0QoA/8LM/05Ev5/rgQlJBuvg1bWYIM0lXbJkurwF7ype+VpUbxHddKWTzi9QVlxoOebtyLHq6ROw6dPjrtIwmZI7h5pcFx1Md/x8MeGcy3FKEciRCbFP+QrXDkQbYTnbvw7gGgBHYZm6FuV+eKnU1tby9u3bh+Orzzlex7J6eHSNRH+IAaQ1yejvh3nglUbi971++52NmZbJzE4UDDI96K1Za2aUOvWnlPPcIMItl07Hax9aGolBwOcWTMU3r71wVNrk/Y4/Ek04I2Wc+SJgRxpEVMfMtbk4dhiN5C5YiYR/wMyHiagSwD/mYjBCknTFEv0e4qhBjiNalR/xajEPX3/RgPwvAzGbKd9Lgq2eIUB604PebVH5a5ze68x4ZVd7sgcJA298dATvfnJ0WCbUXJuK/I4/FCacoZxwgwTGcJiaRrPpLl8JE7V1GMD/0l63wPKRCGkIWr2FfbD9zEreBk6ufTRHdCxuOiXqw9TfSreiDPPQLqtKbV7ljdzyG7ceLqw7z1XPER1vxv1oJ1sTzlBrCkECQ0xNApBGkBBRD9xVCxjAMQBvw8pAP57jseUtQY2AMmkYOmEeUG+uBYicJk0MhHJGD8WKcsnsMqcjn2kyIhFKKf7oN+7+mAkTqVnr3jIxgBUgEGaiGi1mj2yjr4Zaowm6H0dCOLAw/AQKEmYu8W4jojJYyYhrANyZu2HlN34PMZAsQ94fM1PaxYbNG0m3D2CFkj6/vRXPbm0JtRL1OrgHu6KcP63E6msOa9IPEiLecfv1WgfgSgw0KHNoMjAy/QrZkI0JJxcaTdD9KKYmYUBl5Jm5E8A/E9Hv5mg8o4Kgh1htI80fMJj+Ien2URnbYVaieq8Tw6ccSCa8vTHidk6K6tee7liZzs2bGJhJiACpRQUHW012NGg1udBognxsgjDgfiREVDCYz51PBD3E3tpUubArD2Qlqk8Wqo87EG4iDZtjMlgGMxEuqyrPWHI/E6NJqxlOjUY4v0jnI7ndZ3MZrCiu9Tkb0SjB7yHWt+UqHyJoAtZDiZUpyW+yCDuRhskx8fvOgZxrmInQG/68YFoJPmzrDq0ZZTqv4Y6WGi7E9yEMhHSaxa2e1wzgOIAfMvN/5m5I5we5tCt7j60LB9WFsajA3+4dJpNbVd2NGu6+F/r3pvvOoTpvv/DnWDxz8cp0jLRoqeFEfB9CWNI5278OWLW2mPkD/T2/bcLIQ62MD3b1ORM64A6l9dq9M02k3pL3dy2d5dvOVV/Ze79zqCanoPDnMHXDghgJ0VKCkG+E8XX8fwAWh9h2XjKcZox03+2d8PW+64BVsyqo1Px9y+fg5w2HcVP1tLQCIl3VXSfE1943bPjuQAgKfw7rnA9CfAuCMDDS+UiWA/gMgMlE9N+0tybA6nh43jNUZozBCKNM3+2d8OdOGod9R08DSPbf8PuuZ7a0YM27Vrdj9W/J2AJnbGEnyjAhvtleF7/w5+G26YtvQTgfSaeRFAIYb++j55ScBPDVXA4qXxgqx+xghFGm7/au1ptP9DrvpWtr+nr9Idfrte9ZwkQfW9iJMpuVvV6/K13BRu93jISJW3wLwvlGOh/JRgAbiegp6T3iz1CYMYKSFzNN1Jm+W5/w27v6sG5rCwBLG7mzdpbvceuaOzG2wK1sKh+HLqzCTpSqrzshfYKiH3r9rrjJ+N5LuxGJGE4tsXx2YgvCaCOMj+QJIrqTmbsAJ7v9WWb+Yk5HlgcMhRnDKxDKigtDaShhM99VGK6e3Fc9ozSlzLvbp0Konj4By6vK8dSmA4MSlHXNnbhn7SanzMnzdW1Y90D4yd9bv8tkwLQjssSJLQgjizCCZJISIoCV3U5EU8IcnIhuAvBDWD6VJ5j5Bz77XAfgUQAFAI4xc141zBqsGUO3/+sCQddQ+jNMmGG/2+uvUOXag6q4ksn4QvU0PHz9RbixetqgBOXmpuN2JJXFQCf/JbPLcP/Vc7H2vSYwW+Y43ZkuTmxBGDmEESQmEVXaVX9BRLPhLuboCxFFADwG4EYAbQC2EdErzPyRts9EAP8G4CZmbgkroPIRb+KcV+tQlX0bD/c45iSTrVpYQ4ESOkF5IumK8oU1Y+kCZ1lVOQoi5Ggkg9Fontp0AMxAxCCs+vK5b2olCEI4wgiSvwDwvt3gCrCaWz0Y4nNLAexj5iYAIKJnAawA8JG2z70AXlBCipk7wg48n/A61P16haiJUe9/btivhxJVRiQWN10hwEtmu7scDkVZkXUPLs/KR6JKnTBb5VvEiS0II5Mw/Uh+TkSLASyD5av9E2Y+FuLYMwG0aq/bAFzp2ediAAVE9A6syLAfMvOo63XidagTgnuFLKty9z8fTGZ1xlW76oqpdcdUBRz74ya2HTiB+dNKQk/aQRFkko8hCOcHYYsvJgB0ABgDYCERgZnfzfAZ8tnmNYlFASwB8DkAYwFsIqLNzLzXdSCiB2FrQZWVlSGHPHLwToq3L67A7YsrsupK6EeYUOKgKr3ZhDLnYtKXfAxByB8yChIiuh/AHwOoALATlmayCcANGT7aBmCW9roCQLvPPseY+TSA00T0LoDLALgECTOvBbAWsHq2ZxrzSCNoUszWie4ljDAImvSzEQa5mvTFlCUI+UEYjeSPAVwBYDMzX09ElwD46xCf2wZgHhHNBXAQwN2wfCI6LwP4VyKKwkqAvBLAP4cdfD5xLibFMMIgnVDLRhjIpC8I5y9hBMkZZj5DRCCiImb+mIjmZ/oQM8eJ6FsAfgEr/PdJZm4goofs99cw8x4i+jmAD2E113uCmeuzOJ/zmrDCIGjSF2EgCMJgIOb0liIiehHA1wF8G5Y5qxNAATN/Keej86G2tpa3b98+HF8tCIKQtxBRHTPX5uLYYaK2brP/XEVEbwMoBfDzXAxGEARByD/SChIiMgB8yMw1gFN/SxAEQRAcjHRvMrMJYBcR5V/MrSAIgnBOCONsnw6ggYi2AjitNjLzl3M2KkEQBCFvCCNIxgO4RXtNAP4hN8MRBEEQ8o0wgiTq9Y0Q0dgcjUcQBEHIM9K12v1DAH8EoIqIPtTeKgHwQa4HJgiCIOQH6TSSZwC8DuDvATyibe9h5hM5HZUgCIKQN6RrtdsNoBvAPeduOIIgCEK+kTb8VxAEQRAyIYJEEARByAoRJIIgCEJWiCARBEEQskIEiSAIgpAVIkgEQRCErBBBIgiCIGSFCBJBEAQhK0SQCIIgCFkhgkQQBEHIChEkgiAIQlaIIBEEQRCyIqeChIhuIqJGItpHRI+k2e8KIkoQ0VdzOR5BEARh6MmZICGiCIDHANwMYCGAe4hoYcB+/wDgF7kaiyAIgpA7cqmRLAWwj5mbmLkfwLMAVvjs9/8A2ACgI4djEQRBEHJELgXJTACt2us2e5sDEc0EcBuANekOREQPEtF2Itp+9OjRIR+oIAiCMHhyKUjIZxt7Xj8K4DvMnEh3IGZey8y1zFw7efLkoRqfIAiCMASka7WbLW0AZmmvKwC0e/apBfAsEQHAJABfIqI4M7+Uw3EJgiAIQ0guBck2APOIaC6AgwDuBnCvvgMzz1V/E9FTAF4TISIIgpBf5EyQMHOciL4FKxorAuBJZm4goofs99P6RQRBEIT8IJcaCZj5ZwB+5tnmK0CY+b5cjkUQBEHIDZLZLgiCIGSFCBJBEAQhK0SQCIIgCFkhgkQQBEHIChEkgiAIQlaIIBEEQRCyQgSJIAiCkBUiSARBEISsEEEiCIIgZIUIEkEQBCErRJAIgiAIWSGCRBAEQcgKESSCIAhCVoggEQRBELJCBIkgCIKQFSJIBEEQhKwQQSIIgiBkhQgSQRAEIStEkAiCIAhZIYJEEARByAoRJIIgCEJW5FSQENFNRNRIRPuI6BGf979GRB/a//2aiC7L5XgEQRCEoSdngoSIIgAeA3AzgIUA7iGihZ7d9gO4lpkvBfB9AGtzNR5BEAQhN+RSI1kKYB8zNzFzP4BnAazQd2DmXzNzp/1yM4CKHI5HEARByAG5FCQzAbRqr9vsbUH8AYDX/d4gogeJaDsRbT969OgQDlEQBEHIllwKEvLZxr47El0PS5B8x+99Zl7LzLXMXDt58uQhHKIgCIKQLdEcHrsNwCztdQWAdu9ORHQpgCcA3MzMx3M4HkEQBCEH5FIj2QZgHhHNJaJCAHcDeEXfgYgqAbwA4HeZeW8OxyIIgiDkiJxpJMwcJ6JvAfgFgAiAJ5m5gYgest9fA2AlgHIA/0ZEABBn5tpcjUkQBEEYeojZ120xYqmtreXt27cP9zAEQRDyCiKqy9VCXTLbBUEQhKwQQSIIgiBkhQgSQRAEIStEkAiCIAhZIYJEEARByAoRJIIgCEJWiCARBEEQskIEiSAIgpAVIkgEQRCErBBBIgiCIGSFCBJBEAQhK0SQCIIgCFkhgkQQBEHIChEkgiAIQlaIIBEEQRCyQgSJIAiCkBUiSARBEISsEEEiCIIgZIUIEkEQBCErRJAIgiAIWSGCRBAEQciKnAoSIrqJiBqJaB8RPeLzPhHRv9jvf0hEi3M5HkEQBGHoyZkgIaIIgMcA3AxgIYB7iGihZ7ebAcyz/3sQwI8yHXf3wW7MeeQ/UdfcOcQjFgRBEAZDLjWSpQD2MXMTM/cDeBbACs8+KwD8hC02A5hIRNPDHPyOH/1ahIkgCMIIIJeCZCaAVu11m71toPuAiB4kou1EtD3R2+1s39x0fOhGKwiCIAyKXAoS8tnGg9gHzLyWmWuZuTZSXOpsX1ZVnt0IBUEQhKzJpSBpAzBLe10BoH0Q+/iy4Q8/gyWzy7IaoCAIgpA9uRQk2wDMI6K5RFQI4G4Ar3j2eQXA79nRW8sAdDPzoXQHXTSzFAd+8NsiRARBEEYI0VwdmJnjRPQtAL8AEAHwJDM3ENFD9vtrAPwMwJcA7APQC+DruRqPIAiCkBtyJkgAgJl/BktY6NvWaH8zgIdzOQZBEAQht0hmuyAIgpAVIkgEQRCErBBBIgiCIGSFCBJBEAQhK8jyd+cPRNQDoHG4xzFCmATg2HAPYoQg1yKJXIskci2SzGfmklwcOKdRWzmikZlrh3sQIwEi2i7XwkKuRRK5FknkWiQhou25OraYtgRBEISsEEEiCIIgZEU+CpK1wz2AEYRciyRyLZLItUgi1yJJzq5F3jnbBUEQhJFFPmokgiAIwghCBIkgCIKQFXklSIjoJiJqJKJ9RPTIcI9nqCGiWUT0NhHtIaIGIvpje/sFRPQGEX1i/1umfebP7evRSERf1LYvIaLd9nv/QkR+TcRGPEQUIaLfENFr9uvz8loQ0UQiWk9EH9v3x/Lz+Fr8if181BPROiIac75cCyJ6kog6iKhe2zZk505ERUT0nL19CxHNCTUwZs6L/2CVov8UQBWAQgC7ACwc7nEN8TlOB7DY/rsEwF4ACwH8DwCP2NsfAfAP9t8L7etQBGCufX0i9ntbASyH1YXydQA3D/f5DfKa/DcAzwB4zX59Xl4LAP8HwP3234UAJp6P1wJWK+79AMbar38K4L7z5VoAuAbAYgD12rYhO3cAfwRgjf333QCeCzWu4b4wA7iAywH8Qnv95wD+fLjHleNzfhnAjbAy+afb26bDSspMuQawer8st/f5WNt+D4DHh/t8BnH+FQDeAnADkoLkvLsWACbYkyd5tp+P12ImgFYAF8BKqH4NwBfOp2sBYI5HkAzZuat97L+jsKoCUKYx5ZNpS91AijZ726jEVil/C8AWAFPZ7hxp/zvF3i3omsy0//ZuzzceBfBnAExt2/l4LaoAHAXwv20z3xNENA7n4bVg5oMA/glAC4BDsLqq/hLn4bXQGMpzdz7DzHEA3QDKMw0gnwSJn/1yVMYuE9F4ABsAfJuZT6bb1Wcbp9meNxDRLQA6mLku7Ed8to2KawFrZbgYwI+Y+bcAnIZlwghi1F4L2/6/ApapZgaAcUT0O+k+4rNtVFyLEAzm3Ad1XfJJkLQBmKW9rgDQPkxjyRlEVABLiDzNzC/Ym48Q0XT7/ekAOuztQdekzf7buz2fuArAl4noAIBnAdxARP+B8/NatAFoY+Yt9uv1sATL+XgtPg9gPzMfZeYYgBcAfAbn57VQDOW5O58hoiiAUgAnMg0gnwTJNgDziGguERXCcgS9MsxjGlLsyIl/B7CHmf+X9tYrAH7f/vv3YflO1Pa77UiLuQDmAdhqq7c9RLTMPubvaZ/JC5j5z5m5gpnnwPqtf8XMv4Pz81ocBtBKRPPtTZ8D8BHOw2sBy6S1jIiK7XP4HIA9OD+vhWIoz10/1ldhPXeZNbXhdhwN0Mn0JViRTJ8C+IvhHk8Ozu9qWGrkhwB22v99CZaN8i0An9j/XqB95i/s69EILeoEQC2Aevu9f0UIh9lI/Q/AdUg628/LawHgcgDb7XvjJQBl5/G1+GsAH9vn8f/Diko6L64FgHWwfEMxWNrDHwzluQMYA+B5APtgRXZVhRmXlEgRBEEQsiKfTFuCIAjCCEQEiSAIgpAVIkgEQRCErBBBIgiCIGSFCBJBEAQhK0SQCDmHiKYR0bNE9CkRfUREPyOii4d7XABARPcR0YwhOtZEIvqjoTjWIL9/jl4VNs0+92qva4noX3I/OmE0I4JEyCl2wtOLAN5h5guZeSGA7wKYOrwjc7gPVqmNFIgoMsBjTYRVPXUkMweAI0iYeTsz/9fhG44wGhBBIuSa6wHEmHmN2sDMO5n5PQAgoj8lom1E9CER/bW9bQ5ZPTd+bPed+CURjbXf+6+2VvMhET1rb1tFRP+vOj5ZfSrmENE4IvpPItplb7tLHxgRfRVWYtbTRLSTiMYS0QEiWklE7wO4k4gesMe3i4g2EFGx/dmpRPSivX0XEX0GwA8AXGgf6x893zWHrF4i/8ce+3rtWJ+zizHuJqvfRJG9/QAR/QMRbbX/u8je/pQ9dnXsU96Lbn/fe0S0w/7vM/ZbPwDwWXuMf0JE15G718tL9vg2E9Gl2vV9kojeIaImIhLBI7gQQSLkmhoAvoUXiegLsMo2LIWVub2EiK6x354H4DFmrgbQBeAOe/sjAH6LmS8F8FCG774JQDszX8bMNQB+rr/JzOthZYt/jZkvZ+Y++60zzHw1Mz8L4AVmvoKZL4NViuMP7H3+BcBGe/tiAA322D61j/WnPuOZD2CtPfaTAP6IiMYAeArAXcy8CFaBxj/UPnOSmZfCyj5+NMP56nQAuJGZFwO4yx4v7DG+Z4/xnz2f+WsAv7HH910AP9HeuwTAF2H9Vn9FVk04QQAggkQYXr5g//cbADtgTVbz7Pf2M/NO++86WCYZwCoR8jRZFV/jGY6/G8Dn7VX9Z5m5O+S4ntP+rrFX9rsBfA1Atb39BgA/AgBmToQ8diszf2D//R+wSuLMh3Wue+3t/wdW8yLFOu3f5SHHDwAFAH5sj/t5WE2OMnE1rJIjYOZfASgnolL7vf9k5rPMfAyWkBoppklhBCCCRMg1DQCWBLxHAP7eXh1fzswXMfO/2++d1fZLwFqpA8BvA3jMPmYdWRVK43Dfy2MAwJ6cl8ASKH9PRCtDjvm09vdTAL5lawt/rY49SLz1iIJKegd9Rv3tnK/tgyr0+dyfADgC4DJY5ju/fbykKyEe9HsIgggSIef8CkARET2gNhDRFUR0LaxubN8gq/8KiGgmEU0JOA6IyAAwi5nfhtXwaiKA8QAOwDIvgYgWw+pVATsaq5eZ/wNWM6TFPoftgdXWOIgSAIdsU87XtO1vwTZBkdVXfkKIY1USkdIq7gHwPqzig3OU/wPA7wLYqH3mLu3fTfbfB5AUzitgaR9eSgEcYmbTPqYKHEg3xndhnyMRXQfgGKfvhyMIAGRVIeQYZmYiug3Ao0T0CIAzsCbCbzPzJ0S0AMAma2GNUwB+B9aK148IgP+wzS0E4J+ZuYuINgD4PSLaCavdgDITLQLwj0RkwqqW+oc+x3wKwBoi6oO/6eh7sLpUNsPSbNQk/McA1hLRH9jj/UNm3kREH5AVgvu6j59kD4DfJ6LHYVVq/REznyGirwN43tautgFYo32miIi2wFr03WNv+zGAl4loKyyBpmtQin8DsIGI7gTwtrbPhwDiRLTLPvffaJ9ZBasL44cAepEsJy4IaZHqv4JwDiCrdfJrttM/7GcOAKi1/RKCMGIR05YgCIKQFaKRCIIgCFkhGokgCIKQFSJIBEEQhKwQQSIIgiBkhQgSQRAEIStEkAiCIAhZ8X8BLkuEKzIyOM8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What is the correlation of tract usage to tract population?\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(tractPop,tractUse, marker='.' )\n",
    "ax.set(xlabel=\"Census tract population\", ylabel=\"tractUse\")\n",
    "ax.set_xlim(left=0,right=10000)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "91f163ab-6b94-47ee-bd29-be3e751fe2b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# UPDATED VOTES-SEATS CURVE (from votes2seats.ipynb 1/15/22)\n",
    "# LET'S REWORK OUR VOTES - SEATS CALCULATIONS\n",
    "# FIRST, LET'S DO THE BASIC VOTES-TO-SEATS, no fractional seats\n",
    "# INPUTS ARE HDvGOP[nTracts] and HDweight[nTracts]\n",
    "# HDvGOP is the redness lean of each Census tract's Home District\n",
    "# HDweight is the population of this Census tract relative to the state population\n",
    "# stateGOP is the statewide fraction of GOP vote vs. GOP + Dem vote\n",
    "# KEY EQUATION:\n",
    "# expected Seats at a given vote V = sum(HDweight) for tracts with HDvGOP > 0.5 + (V - stateGOP)\n",
    "# for easy calculation, create ~1000 bins, each bin containing HD's in a dV vote window\n",
    "nBins = 1000\n",
    "dV = 1./nBins\n",
    "binWeight = [0.]*nBins\n",
    "\n",
    "for t in range(nTracts):\n",
    "    binNo = int(HDvGOP[t]*nBins)   #which vote bin does this tract's vote go into for the expected statewide vote?\n",
    "    binWeight[binNo] += HDweight[t]\n",
    "    \n",
    "binVote = [0.]*nBins  #this will store the statewide vote for this bin\n",
    "for b in range(nBins) :\n",
    "    binVote[b] = b*dV\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, binWeight, marker='.',linestyle=\"none\")\n",
    "ax.set(xlabel=\"district pct GOP\", ylabel=\"bin weight\")\n",
    "plt.show()   #this should resemble above histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "85608d3f-c3bd-4d91-9378-16270ceeb9b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing with votes-to-seats, let's compute the S(V) curve with no fractional-seat smearing\n",
    "cumVote = [0.]*nBins\n",
    "for b in range(nBins) :\n",
    "    cumVote[nBins-b-1] = np.sum(binWeight[:(b+1)])  #cumulative weight of all Home Districts below vote b*dV\n",
    "    # print(\"bin, bin vote, cumVote for this bin\",b,b*dV,cumVote[b] )\n",
    "    \n",
    "cumSeats = [0.]*nBins  #this will sum the seats earned for a given statewide vote\n",
    "stateVoteBin = int( (stateGOP+0.5*dV)*nBins )   #which bin (out of 1/dV) holds the statewide total vote?\n",
    "for b in range(nBins) :\n",
    "    bb = int( max(0,min(nBins-1,nBins/2 + b-stateVoteBin)) )\n",
    "    cumSeats[b] = 1. - cumVote[bb]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, cumSeats, marker='.',linestyle=\"none\")\n",
    "ax.set(xlabel=\"statewide vote, nBins =\"+str(nBins)+\",\"+str(STATE), ylabel=\"GOP seats won (no fractl smearing)\")\n",
    "RANGE = [0.1, 0.9]\n",
    "fifty50 = [0.5, 0.5]\n",
    "expected = [stateGOP, stateGOP]\n",
    "plt.plot(RANGE,fifty50)\n",
    "plt.plot(fifty50,RANGE)\n",
    "plt.plot(expected,RANGE, linestyle=\"--\",color='red')\n",
    "\n",
    "#LET'S ALSO crudely ESTIMATE RESPONSIVENESS near the STATEWIDE VOTE, with a pseudonormal weighting and lst-sq fit\n",
    "stateSigma = 0.03  #User-adjustable, this is the uncertainty in the statewide vote from election to election\n",
    "usedBins = int(stateSigma/dV)\n",
    "nFitPoints = 6*usedBins\n",
    "voteData = [0.]*nFitPoints\n",
    "seatData = [0.]*nFitPoints\n",
    "counter = 0\n",
    "for b in range(nBins):\n",
    "    if ( abs(b-stateVoteBin) <= 2*usedBins ):  #include this S-V pair in our line fit\n",
    "        voteData[counter]=binVote[b]\n",
    "        seatData[counter]=cumSeats[b]\n",
    "        counter += 1\n",
    "        # print(b,counter)\n",
    "        if ( abs(b-stateVoteBin) < usedBins) : #double count this in data set\n",
    "            voteData[counter]=binVote[b]\n",
    "            seatData[counter]=cumSeats[b]\n",
    "            counter +=1\n",
    "fit = np.polyfit(voteData,seatData,1)  #first-order linear regression with old polyfit\n",
    "Rsimple = fit[0]     #slope is fit[0] in y = mx + b, intercept is fit[1]\n",
    "y0 = fit[1]\n",
    "Rx = [stateGOP - 4.*stateSigma, stateGOP + 4.*stateSigma]\n",
    "Ry = [y0 + Rx[0]*Rsimple, y0 + Rx[1]*Rsimple]\n",
    "plt.plot(Rx,Ry ) \n",
    "ax.text(Rx[1]+0.02, Ry[1]+0.02, \"R = \"+str(round(Rsimple,4)), transform=ax.transAxes, fontsize=14,color='purple')\n",
    "expectedSeats = cumSeats[stateVoteBin]\n",
    "ax.text(0.02,expectedSeats+0.03,\"expected seats = \"+str(round(expectedSeats,3)),transform=ax.transAxes,fontsize=9)\n",
    "ax.text(stateGOP+0.01,0.1,\"HD statewide vote = \"+str(round(stateGOP2,3)),transform=ax.transAxes,fontsize=9)\n",
    "Ex = [0,stateGOP ]\n",
    "Ey = [expectedSeats, expectedSeats]\n",
    "plt.plot(Ex,Ey, linestyle='-.',color='red')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "ce47266f-e2ea-4649-b10c-e8225994c122",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#3/6/22 - alternate votes-to-seats with fractional seat smearing.\n",
    "# First, compute the general cdf sliver for -3 sigma to +3 sigma\n",
    "# Then, apply this smearing to the binVote weights\n",
    "seatVar = 0.04\n",
    "nBins = 1000\n",
    "nSigma = 6.  #how many total sigma of deviation we are explicitly modeling; the rest go into the tails\n",
    "nSlivers = 2*int(3*nBins*seatVar)  #budget for 3 sigma on either side of the expected vote\n",
    "sliverWt = [0.]*nSlivers  #each sliver holds the cdf reflecting the relative distance from the mean vote\n",
    "sliverSigma = [0.]*nSlivers\n",
    "totalSliverWt = 0.\n",
    "for nS in range(nSlivers):\n",
    "    dSigmaHigh = nSigma* (nS+0.5 - nSlivers/2) / float(nSlivers)\n",
    "    dSigmaLow =  nSigma* (nS-0.5 - nSlivers/2) / float(nSlivers)\n",
    "    sliverSigma[nS] = 0.5*(dSigmaHigh+dSigmaLow)  #for plotting; keeps track of this sliver's center sigma\n",
    "    sliverWt[nS] = norm.cdf(dSigmaHigh) - norm.cdf(dSigmaLow)\n",
    "    totalSliverWt += sliverWt[nS]\n",
    "lowSliverWt = 0.5 * (1. - totalSliverWt)\n",
    "highSliverWt = lowSliverWt\n",
    "#print(\"each tail of distribution has weight\",round(lowSliverWt,4) )\n",
    "#plt.plot(sliverSigma,sliverWt)\n",
    "#plt.show()\n",
    "# OK, that worked as expected.  Now, do the smearing of each bin\n",
    "smearedBinWeight = [0.]*nBins\n",
    "for b in range(nBins):\n",
    "    centerBinNo = b #int(HDvGOP[t]*nBins)   #which vote bin does this tract's vote go into for the expected statewide vote?\n",
    "    binOffset = int(nSlivers/2)\n",
    "    for nS in range(nSlivers):\n",
    "        binNo = centerBinNo + nS - binOffset\n",
    "        if (binNo < 0): #deep blue district; variance would push vote %R < 0\n",
    "            binNo = 0\n",
    "        if (binNo >= nBins):  #deep red district; variance would push vote %R > 1\n",
    "            binNo = nBins - 1\n",
    "        smearedBinWeight[binNo] += binWeight[b]*sliverWt[nS]\n",
    "    # Now put the tails into the correct bins\n",
    "    binNo = centerBinNo -1 - binOffset  #left tail\n",
    "    if binNo < 0:\n",
    "        binNo = 0\n",
    "    smearedBinWeight[binNo] += binWeight[b]*lowSliverWt\n",
    "    binNo = centerBinNo + nSlivers - binOffset  #right tail\n",
    "    if binNo >= nBins:\n",
    "        binNo = nBins -1 \n",
    "    smearedBinWeight[binNo] += binWeight[b]*highSliverWt\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, binWeight, marker='.',linestyle=\"none\",label=\"unsmeared\")\n",
    "plt.plot(binVote, smearedBinWeight, marker='o',linestyle=\"none\",label=\"smeared\")\n",
    "RANGE = [0.0, 0.5*np.max(binWeight)]\n",
    "sGOP = [stateGOP, stateGOP]\n",
    "plt.plot(sGOP,RANGE,linestyle=\"-\",label=\"statewide \"+str(round(stateGOP,3)) )\n",
    "ax.set(xlabel=\"district pct GOP\", ylabel=\"bin weight\")\n",
    "plt.legend()\n",
    "plt.show()   #this should resemble above histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "8a2d7de3-ea93-4447-bb69-36defbae38df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing with votes-to-seats, let's compute the S(V) curve with no fractional-seat smearing\n",
    "smearedBinVote = [0.]*nBins  #this will store the statewide vote for this bin\n",
    "cumSmearedVote = [0.]*nBins\n",
    "for b in range(nBins) :\n",
    "    smearedBinVote[b] = b*dV\n",
    "    cumSmearedVote[nBins-b-1] = np.sum(smearedBinWeight[:(b+1)])  #cumulative weight of all Home Districts below vote b*dV\n",
    "    # print(\"bin, bin vote, cumVote for this bin\",b,b*dV,cumVote[b] )\n",
    "    \n",
    "cumSmearedSeats = [0.]*nBins  #this will sum the seats earned for a given statewide vote\n",
    "stateVoteBin = int( (stateGOP+0.5*dV)*nBins )   #which bin (out of 1/dV) holds the statewide total vote?\n",
    "for b in range(nBins) :\n",
    "    bb = int( max(0,min(nBins-1,nBins/2 + b-stateVoteBin)) )\n",
    "    cumSmearedSeats[b] = 1. - cumSmearedVote[bb]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(smearedBinVote,cumSmearedSeats, marker='o',linestyle=\"none\",label=str(seatVar)+\" smear\")\n",
    "plt.plot(binVote, cumSeats, marker='.',linestyle=\"none\",label=\"no smear\")\n",
    "ax.set(xlabel=\"true \"+STATE+\"-wide vote=\"+str(round(stateGOP,4))+\", nBins =\"+str(nBins), ylabel=\"GOP seats won\")\n",
    "RANGE = [0.1, 0.9]\n",
    "fifty50 = [0.5, 0.5]\n",
    "expected = [stateGOP, stateGOP]\n",
    "expectedSeats = cumSeats[stateVoteBin]\n",
    "expectedS = [expectedSeats,expectedSeats]\n",
    "smearedSeats = cumSmearedSeats[stateVoteBin]\n",
    "smearedS = [smearedSeats,smearedSeats]\n",
    "plt.plot(RANGE,fifty50)\n",
    "plt.plot(fifty50,RANGE)\n",
    "plt.plot(RANGE,smearedS, linestyle=\"--\",color='blue',label=str(round(expectedSeats,3))+\"no smear\")\n",
    "plt.plot(RANGE,expectedS, linestyle=\"--\",color='orange',label=str(round(smearedSeats,3))+\"smeared\")\n",
    "plt.plot(expected,RANGE, linestyle=\"--\",color='red')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "597b7758-4f83-40f7-8909-4946e64267ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simpler and fractional-seat-smeared (var of 0.04 ) responsiveness are 2.256 2.328\n",
      "fractional expected GOP seats =  9.668  out of  17 seats for PA\n",
      "simpler expected GOP seats =  9.6025  out of  17 seats\n"
     ]
    }
   ],
   "source": [
    "#Finally, let's compare responsiveness using smeared (fractional seat variance) to non-smeared calculated above\n",
    "#LET'S ALSO crudely ESTIMATE (smeared) RESPONSIVENESS near the STATEWIDE VOTE, as we did for non-smeared\n",
    "stateSigma = 0.03  #User-adjustable, this is the uncertainty in the statewide vote from election to election\n",
    "usedBins = int(stateSigma/dV)\n",
    "nFitPoints = 6*usedBins\n",
    "voteData = [0.]*nFitPoints\n",
    "seatData = [0.]*nFitPoints\n",
    "counter = 0\n",
    "for b in range(nBins):\n",
    "    if ( abs(b-stateVoteBin) <= 2*usedBins ):  #include this S-V pair in our line fit\n",
    "        voteData[counter]=smearedBinVote[b]\n",
    "        seatData[counter]=cumSmearedSeats[b]\n",
    "        counter += 1\n",
    "        # print(b,counter)\n",
    "        if ( abs(b-stateVoteBin) < usedBins) : #double count this in data set\n",
    "            voteData[counter]=smearedBinVote[b]\n",
    "            seatData[counter]=cumSmearedSeats[b]\n",
    "            counter +=1\n",
    "fit = np.polyfit(voteData,seatData,1)  #first-order linear regression with old polyfit\n",
    "Rsmeared = fit[0]     #slope is fit[0] in y = mx + b, intercept is fit[1]\n",
    "y0 = fit[1]\n",
    "\n",
    "print(\"simpler and fractional-seat-smeared (var of\",seatVar,\") responsiveness are\",round(Rsimple,3) ,round(Rsmeared,3) )   \n",
    "print(\"fractional expected GOP seats = \",round(nDistricts*smearedSeats,3),\" out of \",nDistricts,\"seats for\",STATE)\n",
    "print(\"simpler expected GOP seats = \",round(nDistricts*expectedSeats,4),\" out of \",nDistricts,\"seats\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86f82727-7193-4194-aa61-2b99408dbcb6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
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   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
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